Autopoiesis in Organization Theory and Practice (Advanced Series in Management)

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AUTOPOIESIS IN ORGANIZATION THEORY AND PRACTICE

ADVANCED SERIES IN MANAGEMENT

Previous Volumes: Organizations as Learning Systems: ‘‘Living Composition’’ as an Enabling Infrastructure MARJATTA MAULA Complex Systems and Evolutionary Perspectives on Organizations: The Application of Complexity Theory to Organizations ED. EVE MITLETON-KELLY Managing Imaginary Organizations: A New Perspective on Business EDS. BO HEDBERG, PHILIPPE BAUMARD AND A. YAKHLEF Systems Perspectives on Resources, Capabilities and Management Processes EDS. JOHN MORECROFT, RON SANCHEZ AND AIME´ HEENE Tracks and Frames: The Economy of Symbolic Forms in Organizations K. SKOLDBERG Autopoiesis in Organization Theory and Practice EDS. RODRIGO MAGALHA˜ES AND RON SANCHEZ

AUTOPOIESIS IN ORGANIZATION THEORY AND PRACTICE EDITED BY

RODRIGO MAGALHA˜ES Kuwait-Maastricht Business School, Kuwait and Technical University of Lisbon, Portugal

RON SANCHEZ Copenhagen Business School, Denmark and National University of Singapore, Singapore

United Kingdom North America Japan India Malaysia China

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2009 Copyright r 2009 Emerald Group Publishing Limited Reprints and permission service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. No responsibility is accepted for the accuracy of information contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-84855-832-8 ISSN: 1877-6361 (Series)

Contents

List of Contributors PART I

INTRODUCTION

1. Autopoiesis Theory and Organization: An Overview Rodrigo Magalha˜es and Ron Sanchez PART II

vii

3

PERSPECTIVES ON AUTOPOIESIS THEORY

2. Outlining the Terrain of Autopoietic Theory John Brocklesby

29

3. Overcoming Autopoiesis: An Enactive Detour on the Way from Life to Society Ezequiel A. Di Paolo

43

4. Innovation and Organization: An Overview from the Perspective of Luhmann’s Autopoiesis Tore Bakken, Tor Hernes and Eric Wiik

69

5. Autopoiesis and Organizations: A Biological View of Social System Change and Methods for Their Study Chris Goldspink and Robert Kay

89

6. Autopoiesis and Critical Social Systems Theory Christian Fuchs and Wolfgang Hofkirchner

111

vi

Contents PART III

APPLICATIONS OF AUTOPOIESIS IN ORGANIZATION THEORY

7. Productive Misunderstandings between Organization Science and Organization Practice: The Science–Practice Relation from the Perspective of Niklas Luhmann’s Theory of Autopoietic Systems David Seidl 8. Plugging the Theoretical Gaps: How Autopoietic Theory Can Contribute to Process-Based Organizational Research John Brocklesby 9. An Autopoietic Understanding of ‘‘Innovative Organization’’ Tore Bakken, Tor Hernes and Eric Wiik PART IV

133

149

169

APPLICATIONS OF AUTOPOIESIS IN ORGANIZATION PRACTICE

10. Information in Organizations: Rethinking the Autopoietic Account Ian Beeson

185

11. Autopoiesis and the Evolution of Information Systems Marleen Huysman, Heico van der Blonk and Edu Spoor

201

12. The Autopoiesis of Organizational Knowledge, Learning, and Memory Steffen Blaschke

215

13. Autopoiesis: Building a Bridge between Knowledge Management and Complexity Robert Kay and Chris Goldspink

233

14. Autopoiesis as the Foundation for Knowledge Management Paul Parboteeah, Thomas W. Jackson and Gillian Ragsdell 15. Autonomous Cooperation — A Way to Implement Autopoietic Characteristics into Complex Adaptive Logistic Systems? Michael Hu¨lsmann, Bernd Scholz-Reiter, Philip Cordes, Linda Austerschulte, Christoph de Beer and Christine Wycisk 16. The Autopoiesis of Decisions in School Organizations: Conditions and Consequences Raf Vanderstraeten

243

263

289

List of Contributors

Linda Austerschulte

Jacobs University Bremen, Bremen, Germany

Tore Bakken

Norwegian School of Management, Oslo, Norway

Ian Beeson

University of the West of England, Bristol, UK

Steffen Blaschke

University of Bamberg, Bamberg, Germany

John Brocklesby

Victoria University of Wellington, Wellington, New Zealand

Philip Cordes

Jacobs University Bremen, Bremen, Germany

Christoph de Beer

University of Bremen, Bremen, Germany

Ezequiel A. Di Paolo

University of Sussex, Brighton, UK

Christian Fuchs

University of Salzburg, Salzburg, Austria

Chris Goldspink

Incept Labs, Sydney, Australia

Tor Hernes

Copenhagen Business School, Copenhagen, Denmark

Wolfgang Hofkirchner

Vienna University of Technology, Vienna, Austria

Michael Hu¨lsmann

Jacobs University Bremen, Bremen, Germany

Marleen Huysman

VU University Amsterdam, Amsterdam, The Netherlands

Thomas W. Jackson

Loughborough University, Loughborough, UK

Robert Kay

University of Technology, Sydney, Australia

Rodrigo Magalha˜es

Kuwait-Maastricht Business School, Kuwait and Technical University of Lisbon, Lisbon, Portugal

Paul Parboteeah

Loughborough University, Loughborough, UK

Gillian Ragsdell

Loughborough University, Loughborough, UK

viii

List of Contributors

Ron Sanchez

Copenhagen Business School, Copenhagen, Denmark and National University of Singapore, Singapore

Bernd Scholz-Reiter

University of Bremen, Bremen, Germany

David Seidl

University of Zurich, Zurich, Switzerland

Edu Spoor

VU University Amsterdam, Amsterdam, The Netherlands

Heico van der Blonk

University of Groningen, Groningen, The Netherlands

Raf Vanderstraeten

Ghent University, Ghent, Belgium

Eric Wiik

Norwegian School of Management, Oslo, Norway

Christine Wycisk

University of Bremen, Bremen, Germany

PART I INTRODUCTION

Chapter 1

Autopoiesis Theory and Organization: An Overview Rodrigo Magalha˜es and Ron Sanchez

We human beings are not rational animals; we are emotional, languaging animals that use the operational coherences of language, through the constitution of rational systems, to explain and justify our actions, while in the process and without realizing it, we blind ourselves about the emotional grounding of all the rational domains that we bring forth. (Humberto Maturana, 1988, p. 787)

1 Introduction This introductory chapter elaborates some of the key ideas which shaped the concept of this book. The overriding idea is that autopoiesis theory has the potential to provide a unifying framework for the study of organizational phenomena in the 21st century. Although organization studies have recently had no shortage of new paradigms and approaches — such as postmodernism, phenomenology, ethnomethodology, reﬂexivity, and critical theory — the ﬁeld seems to be expanding in ways that make it increasingly difﬁcult to comprehend, especially for the uninitiated. In the 1950s and 1960s, open systems theory, together with sociological systems theory, was enormously inﬂuential in providing a coherent framework for the study of organizations and their environments. These approaches were in important respects motivated by ideals of order, stability, and predictability. So inﬂuential were they that the paradigm they deﬁned is still prevalent today. Although today’s organizations and their environments are often characterized by transformation, emergence, much unpredictability, and a strong emphasis on people, the systems approach to understanding organizations is still not being conveyed in a coherent manner, especially to students and managers. The reason for this, in our view, is the

Autopoiesis in Organization Theory and Practice Advanced Series in Management, 3–25 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006002

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Rodrigo Magalha˜es and Ron Sanchez

lack of a unifying framework for explaining a spectrum of organizational phenomena, from stable to highly dynamic organizations and environments. Autopoiesis is a concept developed through the pioneering work of Maturana and Varela (1980, 1992) in biology, primarily as a construct which enabled a distinction to be made between living and nonliving systems. The concept and its postulates have slowly been gaining ground and generating enthusiasm among many scientiﬁc communities. For Fritjof Capra, for example, Maturana and Varela’s book The tree of knowledge (1992) contains no less than the ‘‘outlines of a uniﬁed scientiﬁc conception of mind, matter and life’’ (on book’s back cover). King (1993) suggests that autopoiesis is developing into a new theoretical paradigm in the social sciences, and von Krogh and Roos (1995b) suggest that autopoiesis offers the basis for a new general systems theory. We believe that the organization of the future needs an epistemology (i.e., a theory of organizational knowledge) which is radically different from epistemologies that have guided organizational thinking hitherto, and that autopoiesis theory, with due adaptations, can furnish such an epistemology. In this chapter we begin by providing a brief overview of the key tenets of autopoiesis theory applied to organizational settings. Next, we discuss the organization of the future, starting with the external pressures that are increasingly being exerted on social organizations of all types and inducing them to undertake new kinds of transformations. This chapter identiﬁes important challenges facing organizational thinkers, now and in the foreseeable future, that exist not only as the result of the external pressures, but also as a consequence of internal developments in organization science and theory. The chapter concludes with an overview of the topics addressed by the contributors to this volume.

2 Key Tenets of Autopoiesis Theory Applied to the Study of Organizations For many people, the adoption and use of concepts from autopoiesis theory in organizational analysis is uncontroversial. However, some organization scholars are reluctant to adopt autopoiesis concepts or theory because of concerns as to whether organizations really are autopoietic systems (Mingers, 2002, 2004). Thus, while some authors choose to apply autopoiesis theory to organization studies following strict ontological principles, others are less convinced theoretically and treat autopoiesis as a metaphorical perspective. The controversy between these two approaches is somewhat surprising given the freedom with which metaphors are used in much organizational theorizing (Morgan, 1997). The reluctance that many authors show in applying autopoiesis to social settings may be partly due to the fact that both Maturana and Varela stated in their writings that autopoiesis is not a social theory. From his personal contacts with Humberto Maturana, Zeleny (2007) has added a further perspective, suggesting that the creators of autopoiesis were so careful in pointing out that the theory should be left out of the

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5

social domain because of the political climate of Chile in the 1970s. Zeleny suggests that Maturana and Varela may have been apprehensive about the misuse by the prevailing political powers of the mechanistic nature of their theoretical propositions in the biological sciences in the social sphere. Nevertheless, in the following, we provide an overview of the principal applications of autopoiesis theory in organizational settings.

2.1

Organization and Structure

In the organizational world, there are forces which are informal, enduring, and hard to change (e.g., cultural norms), and others which are formal, often ephemeral, and more amenable to adoption (e.g., processes, procedures, and tasks). The latter are inevitably inﬂuenced and shaped by the former. In organizational theory and research, these two kinds of forces are usually treated separately, because it is often very difﬁcult to reconcile them, although from the point of the practitioner, this is always disappointing. Autopoiesis theory, however, offers organizational theorists and researchers new possibilities to address such disparate organizational phenomena in a much more integrated fashion. Take the concepts of organization and structure, for example. Within the autopoietic perspective, organization means necessary relationships or network of rules that govern relations between system components and that thereby deﬁne the system conceptually. Structure means the actual relations between the components that integrate the system in practice and that satisfy the constraints placed by the organization. Using the tenets of autopoietic theory, Zeleny (2005) interprets organizations as networks of interactions, reactions, and processes identiﬁed by their organization (network of rules of coordination) and differentiated by their structure (speciﬁc spatio-temporal manifestations of applying the rules of coordination under speciﬁc conditions or contexts). Following these deﬁnitions, Zeleny argues that the only way to make organizational change effective is to change the rules of behavior (i.e., the organization) ﬁrst, and then change processes, routines, and procedures (i.e., the structure). He explains that it is the system of the rules of coordination, rather than the processes themselves, that deﬁnes the nature of recurrent execution of coordinated action (recurrence being the necessary condition for learning to occur). He states: ‘‘Organization drives the structure, structure follows organization, and the observer imputes function’’ (ibid, p. 197). Espejo, Schumann, Schwaninger, and Bilello (1996) adopt similar terminology, but instead of organization they refer to an organization’s identity as the element that deﬁnes any organization, explaining that it is the relationships between the participants that create the distinct identity for the network or the group. Organization is then deﬁned as ‘‘a closed network of relationships with an identity of its own’’ (ibid, p. 75). Like Zeleny, Espejo et al. (1996) also see the organization’s structure as being the differentiating factor. While organizations may share the same kind of identity, they are distinguished by their structures. People’s relationships form routines, involving roles, procedures, and uses of resources that constitute stable

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forms of interaction. These allow the integrated use and operation of the organization’s resources. The emergent routines and mechanisms of interaction then constitute the organization’s structure. Hence, just like any autopoietic entity, organizations as social phenomena are characterized by both an organization (or identity) and a structure. The rules of interaction established by the organization and the execution of the rules exhibited by the structure form a recursive bond. The adoption of autopoietic notions of organization and structure by conventional organization theory may create exciting new opportunities to establish theoretical and practical links between the structurally determined or ‘‘engineered’’ parts of an organization, such as its business processes, and the emergent properties arising from the actions and interactions of human actors that jointly shape the organization’s identity. Our understanding of the heterogeneous engineering (Law, 1987) of the multitude of soft and hard aspects of social organizations can greatly beneﬁt from an elaboration of this dichotomy and the ways in which the two dichotomous parts interact and inﬂuence each other. We return to this point below.

2.2

Operational Closure, Self-Referentiality, and Recursivity

An autopoietic system is deﬁned as a system that is generated through closed organization processes of production such that the same organization of processes is reproduced through the interactions of its own products (components). Thus, the organization of components and component-producing processes may remain relatively invariant through the interactions and turnover of components. If an organization (the speciﬁed relations between components or processes) were to change substantially, there would not necessarily be a change in that system’s identity. What would change is the system’s structure (its particular manifestation in the given environment) within the degrees of freedom allowed by the speciﬁed relations between components. In this way, the development of a system’s structure is done recursively. In order to enable the evolution of structure through such recursive behavior — which is the essence of autopoiesis — the autopoietic system needs to be operationally closed (Zeleny, 2003). Mingers (2001) argues that although autopoiesis cannot be applied as a whole to social theory, there are some key principles of autopoiesis that are applicable, namely the principle of an organization’s operational closure. This argument is based on the assumption that throughout the entire hierarchy of systems proposed by Boulding (1956), all levels of systems exhibit characteristics of organizational closure. As we have explained above, in autopoiesis the main requirement for identifying living, autonomous systems is not the existence of a set of inputs and outputs, but an internal coherence that results from the interconnectedness of a system’s inputs and outputs (Varela, 1984). In this respect, organizational closure ‘‘requires some form of selfreference, whether material, linguistic, or social, rather than the more speciﬁc process of self-production’’ [emphasis added] (Mingers, 2001, p. 111). Organizational closure and self-referentiality are criteria that unequivocally deﬁne social systems. The various institutional systems and subsystems that make up a

Autopoiesis Theory and Organization: An Overview

7

social system become closed domains of communication, autonomous and independent, while maintaining strong forms of interdependence (structural couplings) because they rely on each other to perform many societal functions. Interactions between subsystems are often quite well deﬁned, for example, in business organizations. Communications about the environment may give rise to strategic marketing communications that, in turn, trigger communications among product development, capital budgeting, and production subsystems. Such communication activity arises from interactions among organizational actors that may enhance or constrain further communication activity. Understanding organizational closure is one of the most important insights that an autopoietic perspective can bring to organizational analysis. The inﬂuence of open systems theory has sometimes helped to popularize the notion that organizations are wholly open systems. However, there is host of organizational phenomena that cannot be explained as open systems phenomena, but that can be explained through autopoietic systems theory’s concept of organizational closure. Organizational culture, for example, cannot be adequately explained by invoking the principles of open systems theory. In more practical terms, an understanding of the closed and recursive nature of its broader social system is crucial for an organization’s actors to understand the environmental impacts of the organization’s activity. Organizational closure also provides the conceptual and practical foundation for studying the systemic feedback loops that writers such as Argyris (1977), Senge (1990), and Sanchez and Heene (1996) have reinterpreted for the managerial world.

2.3

Structural Coupling

Goldspink and Kay (2004) suggest that autopoiesis also provides the basic concepts for understanding the mechanics of sociality and therefore of organizations. They state: Humans exist in and through domains, which are the product of their structural coupling with an environment. This environment is the world around them, including other humans, and exists both physically and causally. As humans enter into reciprocal interaction over time there emerges, as a consequence of structural coupling, a certain alignment of their behaviors, including their linguistic behaviors. Hence we can refer to the resulting domain as a consensual domain. This domain now forms the basic unit of social analysis, and exists in a causal sense but not in a physical one. (ibid, p. 605)

Human beings are autopoietic, which means that as individuals we are all operationally closed. To illustrate, we have all experienced occasions when no matter what we say and explain to our dialoguing counterpart, he or she is unable to comprehend our point of view. This situation can last for a few minutes, or hours, or may endure for years and even lifetimes. Operational closure can be observed in our daily interactions at work, in the shopping mall, and in the family. The only way to overcome autopoietic closure is by building structural couplings. The nature and degree of structural coupling that emerges when two or more individuals interact is a

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deﬁning feature of the macro system of invisible rules and procedures that characterize social institutions. Organizational closure, however, should not be confused with the notions of ‘‘closed’’ and ‘‘open’’ systems from traditional systems theory. Maula (2006) argues that openness and closure are not only simultaneous phenomena, but they also necessitate each other. In other words, there are no environmentally ‘‘closed’’ systems. An organizational closed system cannot be completely closed to its environment, because it cannot be completely unresponsive to environmental signals and perturbations. Organizational closed systems are therefore closed with respect to their own organization and structure, but they may nevertheless maintain intense interactions with the environment. Through recurrent environmental signals, perturbations, and triggers, a system becomes coupled to its environment. Such coupling is achieved through changes in the system’s structure, even while the organization remains autonomous and closed (Zeleny, 2003). Structural coupling is an essential concept in understanding the decentralized networked organization. The concept of coupling provides the basis for understanding how an organization may be fragmented in time and space while retaining its unity as a system. This autopoietic concept has a direct counterpart in the organizational terminology created by Weick (1976) when he refers to ‘‘loose coupling.’’ He asserts that organizations are loosely coupled systems and deﬁnes loose coupling as ‘‘a situation in which elements are responsive but retain evidence of separateness and identity’’ (Orton & Weick, 1990, p. 203). In autopoietic terminology, ‘‘responsiveness’’ refers to compensating actions in response to perturbations from the environment. ‘‘Separateness and identity’’ refer to the maintenance of the network of interactions that deﬁnes the organization of the system. Orton and Weick (1990) further propose loose coupling in organizational design requires more modularity in organizational design (Sanchez & Mahoney, 1996), a broader range of requisite variety (Ashby, 1956), and greater behavioral discretion. They also suggest that an organization’s compensatory mechanisms for loose coupling may include enhanced leadership, focused attention, and shared values. The implications of the notions of structural coupling and loose coupling for organization theory are very signiﬁcant. They underpin the representation of organizations as composites for which words such as bricolage or assemblage are increasingly being used as descriptors (Ciborra and Associates, 2000).

2.4

Language and Languaging

Von Krogh and Roos (1995b) made one of the most signiﬁcant contributions to integrate autopoiesis into management theory and research. In so doing, they advanced an anticognitivist position in the organizational knowledge debate. They reject the notion that knowledge is a given and that the task of organizational systems is to represent it as accurately as possible. Instead, they argue that knowledge is embodied in human beings and that representations of the world in the human mind

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come forth as a result of actions or observations by human beings. This point is illustrated by the often cited statement by Maturana and Varela (1992), ‘‘Knowledge is what brings forth a world.’’ The following passage from von Krogh and Roos (1995b, p. 52) exempliﬁes this crucial issue: Imagine that you are about to enter an ofﬁce that is new to you. Your experience (knowledge) tells you to take an initial sweeping look in order to locate the reception desk, your assumed point of entry into the inner circles of the ofﬁce. Having located what you believe is the reception desk (world) you take the ﬁrst steps towards the desk. In doing this you get a glimpse of a corridor on your right-hand side in which you see a door and on which you locate a name plate (world). You recognize the name on the door to be the person you are supposed to visit (knowledge).

The ideas that the world is brought forth in knowledge and that knowledge is not abstract but is embodied in human action frame the discussion about individual versus organizational knowledge (Sanchez, 2001; Sanchez & Heene, 2006). Von Krogh and Roos (1995b) argue that the bridge between socialized and individualized knowledge is achieved by means of language. Language is what allows action to be coordinated in the organization, and such coordination is achieved through organizational members making useful distinctions about the organization (an important form of organizational learning). The ﬁrst and broadest distinction is the concept of ‘‘organization’’ itself. Linguistically, the organization has to be distinguished from its environment. The emergence in social interactions of a new entity, in this case the organization, presupposes a languaging capability. Organizational members conceive of the organization they are working for through language, and from this very broad distinction (i.e., the organization from the environment), ﬁner distinctions can start to be made. For example, there will be linguistic distinctions associated with the concept of ‘‘product’’ in a given organization. In this way, an organization develops its own languaging process and resulting language that conveys its own system of meaning. An organization’s language-enabled system of meaning, in turn, develops its own autopoiesis. ‘‘Languaging’’ is the expression used by Maturana and Varela (1980, 1992) to denote the act of using language. Given its dynamic nature, languaging fulﬁls a dual but conﬂicting function. On one hand, because languaging contributes to creating a unique identity for an organization (e.g., language is integral to its culture), languaging can be instrumental in bringing about change. On the other hand, language is important in maintaining the status quo and may thereby be a source of resistance to change, given the self-referential nature of autopoietic systems. Hence, ‘‘to allow for rules and languaging that give way for effective action’’ (von Krogh & Roos, 1995b, p. 101) is one of the main goals for and functions of socialized organizational knowledge. Von Krogh and Roos (1995a) suggest that knowledge development in organizations comes about through the innovative use of old and new words and concepts — for example, through managerial efforts to shape language development in an organization.

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2.5

Emotions and Emotioning

One form of communication in organizations is the conversations that can take place between two or more persons. When conversations happen and become recurrent among the same group of people, a social network, group, or community is formed. Conversations allow a structuration process (Giddens, 1984) to evolve, and once the structure of the network is formed, conversations become organizationally closed and self-referential. Metaphorically speaking, conversations have embedded in them the genetic code of a social network, through the three elements of structure — signiﬁcation, domination, and legitimation (Giddens, 1984). The internal dynamics, roles, and values of networks, groups, and communities develop through conversations. Hence, for a newcomer to become part of a group — a behavioral domain — he/she has to learn, through participation, the group’s genetic code and his/her role that is implicit in it. In this way, the social individual becomes structurally coupled to the social network. Social membership means accepting the unwritten rules of a group and (thereby) being accepted by the group.1 Without mutual acceptance on some basis, cooperation and social action are not possible. Social boundaries, social norms, and emerging social practices transcend the individual and remain even after individuals have departed. Particular members may join or leave, but the social organization carries on. Moreover, organizations are based on self-transcendence — the reaching out beyond one’s own existence in order to create shared understandings with others. In empathizing with colleagues or customers in the process of socialization, the boundaries between individuals are diminished. In the process of committing to a group and becoming part of the group, the individual transcends the boundaries of the self. In the process of internalizing organizational knowledge, individuals cross the boundaries and enter the domain of the group or an organization (Nonaka et al., 2001). The notion of boundaries of social systems implies a complementary notion of organizational contexts. Context can be understood as a situation in which individuals, work teams, or an organizational unit exerts a signiﬁcant inﬂuence on internal and external interpersonal relationships. Kakabadse and Kakabadse (1999, p. 7) assert that ‘‘the power of context is substantial, for context helps form the attitudes and perspectives individuals hold about life, work, people, and organization.’’ Viewed through the lens of autopoiesis theory, the notion of organizational context can be seen in a new light. Maturana (1988) argues that emotions form the background for the embodiment of all our knowledge and thus cannot be separated from logical thought in everyday action. For Maturana, emotions are the ingredient 1

Maturana (1988) argues that decisions about acceptance and rejection by groups are likely to be emotional rather than rational – i.e., emotions are the ingredient that makes social phenomena possible, through mutual acceptance (love, in his terminology). Sanchez suggests (here), however, that this view may also reﬂect Maturana’s Latin cultural context in Chile, and that other cultural contexts may emphasize more rational bases for acceptance or rejection of members by social groups (e.g., trust among managers in a network of frequently transacting ﬁrms, commitment to adhere to the norms of a professional group, and a recognized common interest in a coordinated community response to an opportunity or threat).

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that makes all social phenomena possible, through mutual acceptance. However, in our western-style management we have evolved a paradigm that encourages the separation of logic and emotion. One of the earlier voices to denounce this state of affairs was Selznick (1957, p. 80): ‘‘The importance of values is afﬁrmed but the choice of goals and of character-deﬁning methods is banished from the science of administration.’’ However, this situation may be changing — for example, through the emergence of the idea of karma capitalism (which we will revisit further on) as exempliﬁed by the notions of soft power and smart power put forward by Nye (2008). Such movements suggest that there is a renewed perception of the importance of intangible elements like attitudes, emotions, and values in the workplace. The merging of the economic and emotional contexts of ﬁrms, for example, is at the heart of the holistic representation of ﬁrms in new strategic management theory (Sanchez & Heene, 2004).

3 Organizations and Organizing in the Future In this section we propose a view of the organization of the future in two important dimensions. First, we consider external pressures at the macro level that will increasingly exert a force for change for all types of organizations. Second, we suggest some major internal organizational challenges that both organizational researchers and practitioners will be challenged to face in the foreseeable future. Of course, both the external pressures and the internal organizational challenges are interrelated (see Figure 1).

3.1

New External Pressures

James March (2007) suggests that the ﬁeld of organization studies may be entering a fourth ‘‘invasion’’ era characterized by the growing inﬂuence of information technologies and biological advancements on social life and by the earth’s declining ability to sustain the current conduct of the rapidly growing human species.2 We agree with this broad assessment and suggest that four trends will be decisive in shaping the organization of the future:

2

The earth’s declining capacity to sustain the current practices of the human species; New kinds of capitalism, leading to the individualized corporation; Technical and social networking as the basis for decentralized, autonomous organizational forms; A world fuelled by ubiquitous, real-time data and information.

The previous critical landmarks according to March (2007) were (1) the Second World War; (2) the social and political protests of the 1960s and early 1970s; and (3) the collapse of the Soviet Empire and the triumph of the markets.

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Sustainability of the Earth External pressures

Search for new organizational paradigm

The networking nature of organizing and organizations

Practice turn, trans -disciplinarity and multi-disciplinarity New epistemological approaches inspired on nonlinearity and complexity

The integration of the social and the technological architectures

Technical and Social Networking

New Kinds of Capitalism

Organizational trends

Ubiquitous, real-time information

Figure 1: Major external pressures and key challenges of the organization of the future.

In particular, we suggest that the environmental issues associated with global warming are bound to have a marked effect on many aspects of organizations and organizational life. A central challenge for organization researchers therefore will be to understand how organization studies can contribute toward a world that is sustainable, not only in business terms, but more fundamentally in terms of the survival of the human species. The role of organizations in dealing with the earth’s declining ability to sustain the human species will depend to a large degree on the approaches and concepts adopted by organizations’ managements on a global scale. A related trend is the emergence of new attitudes and values in capitalism as a basis for economic organization, as exempliﬁed by the development known as karma capitalism. The growing awareness of environmental issues that at least some corporations have been displaying in recent years is helping to bring about a new business ethos, which can be characterized as a more socially, environmentally, and morally concerned approach to business. In the past many companies would bow only to the demands of their shareholders and customers; increasingly, however, companies are forced to consider their impact on everyone with a direct or indirect interest. Growing numbers of business scholars are advising executives to pursue broader purposes than just making money and are urging companies to take a more holistic approach to business, taking into account the needs of shareholders, employees, customers, society, and the environment (Sanchez & Heene, 2004). Developments like karma capitalism reﬂect the convergence of many global trends. The Nobel Peace award to Muhammad Yunus for promoting micro credit as a new

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form of capitalism for the very poor also reﬂects these trends. However, capitalism is also changing from within the ﬁrm, often leading to dramatic changes in the relationship between the ﬁrm and the individual. Such changes are captured in the notion of The individualized corporation as proposed by Ghoshal and Bartlett (1998). More than ever before in some ﬁrms, the individual worker is becoming the center of management concerns. This trend is due to the ongoing shift in the economy from traditional industries based on manual workers to new enterprises based on knowledge workers who are now the crucial asset in many businesses. At the same time, while many corporations can no longer guarantee employment, growing numbers of knowledge workers no longer need or are even concerned about guaranteed employment. As a result, the nature of the bond between the organization and its employees is changing radically, and the notion of a ‘‘moral contract’’ between a ﬁrm and its employees is beginning to replace legal contracts as the basis for employment (Ghoshal & Bartlett, 1998). The organizational world is also being transformed by phenomena that run counter to the traditional command and control model of organization. Although the idea of the ‘‘networked organization’’ has been a topic of discussion for a couple of decades, we are now entering an era in which we can observe real decentralized, autonomous, networked organizations on a global scale. What is important in this development is that not only organizations as institutions are able to network with other organizations, but people are now able to network person-to-person as never before. Internet and mobile telecom technologies are enabling people to meet and to coordinate their activities in ways that are profoundly affecting their lives, both professional and private. Perhaps the best example of the positive potential of a global, decentralized, autonomous, networked organization is the World Wide Web. However, other examples such as Al-Qaeda, where individual networking capabilities seem to play a predominant role in organizing, show that such developments are not limited to corporate or high-tech domains. Both examples not only follow a decentralized, networked form, but also lack any kind of conventional management structure. In a similar manner, the Linux phenomenon has no formal structure, employees, or budgets, and its product is free. Yet Linux is already posing a serious threat to the largest software ﬁrms in the world (Hernes & Bakken, 2003). Networking (individual and institutional) is of course intimately related to the proliferation of information technology (IT) in human society. Ever faster enterprise LANs, telephony over IP data networks (VOIP), mobile telephony, home networks, and Internet access in automobiles, planes, and trains are all having a major impact on an organization’s and an individual’s capability to transmit information. As citizens of the world, we are increasingly surrounded by real-time or near real-time data and information. When an ice sheet breaks loose in the Arctic, citizens of Africa learn about it a few minutes later. If a car breaks down in Outer Mongolia, the manufacturer’s assistance services in Europe will be alerted instantaneously. Online DNA data is being used by international police forces to solve crimes in a fraction of the time it took to solve similar crimes in the past. An even greater effect of IT than the ubiquity of information is its ability to represent large chunks of organizational life as information. Balanced scorecards,

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dashboards, value added analytics, customer relationship management systems, early warning systems, trend monitoring, and knowledge management are examples of systems that facilitate representation of many aspects of an organization. The ever increasing capability to collect, organize, transmit, and use information anywhere is bringing the notion of the real-time enterprise into reality (Kuhlin & Thielmann, 2005). There is a growing realization, however, that organizations need to harness such information by developing more sophisticated representational techniques to enable hitherto unthinkable levels of organizational self-awareness (Magalha˜es, Sousa, & Tribolet, 2008).

3.2

New Internal Organizational Challenges

3.2.1 New Epistemological Approaches Inspired by Non-linearity and Complexity. Complexity research has its roots in long-standing traditions in economics (Adam Smith’s hidden hand), natural evolution (Darwin’s blind watch making), neuropsychology (Hebb’s cell assembly), and computation (von Neumann’s selfreproducing automata). From the point of view of the social sciences, complexity theory does not try to make detailed predictions, but rather raises new kinds of questions and possible organizational actions. Analyzing social systems from a complexity perspective does not ensure the derivation of speciﬁc outcomes, but may ‘‘foster an increase in the value of populations over time, whether populations are livestock, technical innovations, or new strategies for business competition’’ (Axelrod & Cohen, 2000, p. 19). Experiments in artiﬁcial intelligence, for example, have shown that emergence and self-organization are implicate order phenomena which follow a bottom-up, parallelprocessing, distributed-control logic in which local interactions within populations of semiautonomous entities are usually governed by a system of simple rules. When recursively applied to individual behaviors and interactions among the components of a system, unpredictable global behavioral patterns may be observed under certain conditions. Human waiting queues often exhibit, at least temporarily, the voluntary self-organization characterized by its own speciﬁc behaviors, rules of conduct, choice of interpersonal distance, and modes of communication. In short, as has been suggested by Gell-Mann, we are able to observe ‘‘surface complexity arising out of deep simplicity’’ (Lewin, 1992, p. 14). In organizations the situation is analogous — i.e., whatever phenomenon one wishes to study will always be dependent upon a higher-level context, which in turn has dynamic links with the event under scrutiny. In effect, one needs to understand how individual components contribute to the behavior of a holistic organizational context, but it is also crucial to understand how the context inﬂuences the behavior of each individual component. As a case in point, consider how a system of local trading agents develops prices that cause global inventories to clear or how companies form networks of trust that ensure individual customers’ loyalty and continued growth (Axelrod & Cohen, 2000).

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Autopoiesis has a counterpart in the cognitive sciences — enacted cognition — which may also be considered part of the complexity paradigm. Autopoiesis and enacted cognition are interrelated because in explaining the evolution of living organisms, autopoiesis invokes the notion of enacted cognition (Varela, Thompson, & Rosch, 1991) to suggest how human beings understand the world and how knowledge is formed. For Varela et al. (1991), cognition cannot be understood without ‘‘common sense,’’ by which they mean our physical and social history, the mutual co-speciﬁcation between the knower and the known or between the subject and the object. They use the term enactment to denote interpretation or the act of bringing forth meaning from a background of understanding. They adopt a nonobjectivist view of knowledge, in which knowing is the result of an ongoing interpretation process that emerges from our ability to understand and which enables us to make sense of our world. The notion of the embodiment of cognition has been strongly inﬂuenced by the philosophy of European thinkers such as Heidegger, Merleau-Ponty, and Foucault, who since the beginning of the 20th century have challenged one of the most entrenched suppositions of our scientiﬁc heritage — i.e., the rationalists’ view of the world as independent from the knower. The application of complexity concepts and theory to economics, management, and organization has attracted a great deal of interest and generated a large number of academic communications (see, for example, the specialized journal Emergence: Complexity & Organization, E:CO). At the risk of oversimplifying the issue, we mention the key conclusions from one of the earliest investigations in the ﬁeld. Trisoglio (1995) posits that economics and much of management theory is based on reductionist, linear, and equilibrium-centered models of the world. Although they may be simple, such models would be seriously misleading as descriptions of a ‘‘reality’’ that is manifestly often nonlinear. Economies and organizations routinely manifest the properties of nonlinear and chaotic systems, exhibiting creation of both order and disorder as well as pattern and regularity. The link between autopoiesis, complexity, and social organizations has been described by Goldspink and Kay (2004): Autopoiesis provides a model of how phenomena (which we may now call social phenomena) emerge from the complex (and non-linear) interplay between the heterogeneous (in having unique ontogenies) agents (people) which make it up. Complexity then allows us to explain the resulting dynamics by describing the generative processes that link empirical observation and causal actuality. Social systems can be seen as a speciﬁc class of complex systems and it is autopoiesis which clariﬁes the distinguishing characteristics of this class, in particular the linguistic/reﬂexive character of social agents. (ibid, p. 615)

3.2.2 The Search for a New Organizational Paradigm. The situation in organization science/studies today is that the overwhelming majority of textbooks used in graduate and postgraduate courses convey linear, reductionist representations of organizations that are typical of what we might call the old paradigm. An example is the book by Scott and Davis (2007) which undertakes to bring together much of the accumulated wisdom in rational, natural, and open systems views of organizations. Although warning about the dangers of clinging too much to the past when trying to

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move toward the future, these authors nevertheless present an overview of organization theory that offers a fairly simplistic divide between the old (closed systems) and the new (open systems); see Table 1. As is demonstrated by continuing organizational failures in every area of activity, open systems theories have not ‘‘solved’’ the problems of organizations. One reason for this may be the current relative neglect of ‘‘old fashioned’’ closed systems approaches, according to the summary of schools of thought expounded by Scott and Davis (2007). Although open systems have brought crucial advances in organizational thinking — especially in fostering new understanding of the contingent nature of organizational designs — the open systems view still has major unresolved problems. Behind this state of affairs may be the way that the open systems framework has been utilized by the research community in the organizational sciences. Almost three decades ago, Pondy and Mitroff (1979) pointed out that ‘‘we have seriously misunderstood the nature of open systems and have confused them with natural or control systems’’ (ibid, p. 22), and that ‘‘organization theorists seem to have forgotten that they are dealing with human organizations, not merely disembodied structures in which individuals play either the role of in-place metering devices (y) or the role of passive carriers of cultural values and skills’’ (ibid, p.17). These comments by Pondy and Mitroff were early warnings about the inherent limitations of an emerging new organizational paradigm. Three decades later, however, these problems persist. So, why is change moving so slowly? Is the debate between open versus closed systems ended? Why do we ﬁnd it so hard, as an intellectual community, to move away from overly simplistic input–output organizational models — as if the issue of the interchange between the organization and its environment were clear-cut and unproblematic? Given the current institutionalization of organization studies, can autopoiesis theory play a role in renewing systems theory as applied to organization studies? 3.2.3 The Turn Toward Practice, Transdisciplinarity, and Multidisciplinarity. Organization theory is paradigmatic because of its deep (and sometimes unrecognized) underlying assumptions. The organization — a concept — can be researched from a considerable number of perspectives, as Morgan’s (1997) metaphors amply illustrate. A tendency within the ﬁeld has therefore been to adopt multiparadigm or transdisciplinary research approaches that do not favor any one approach. Gibbons et al. (1994) suggested that there are two opposing modes of knowledge production in society. Mode 1 refers to the traditional practices of science and research focused on developing and testing theories about the social world, while Mode 2 focuses on developing ideas that have relevance and can be applied in contemporary organizations. Many proponents hold a view that the organization sciences are moving toward Mode 2, and that they need to be not only more transdisciplinary but also more problem-oriented. The turn toward Mode 2 for knowledge production comes with a strong assumption that a similar turn is under way in the theories of organization and strategy (Whittington, 2006). However, as we are well aware, an organization is a social construction that cannot be engineered as neatly as a bridge or a molecule.

1900–1930, Rational models

1930–1960, Natural models

Conﬂict Models (Gouldner, 1954)

Human Relations (Mayo, 1945)

Cooperative Systems (Barnard, 1938)

Human Relations (Whyte, 1959)

Administrative Theory (Fayol, 1949)

Scientiﬁc Management (Taylor, 1911) Decision Making (Simon, 1945/ 1997) Bureaucratic Theory (Weber, 1947)

Closed systems models

Table 1: The conventional wisdom of organization theory. Levels of analysis

Social Psychological

Structural

Ecological

Source: Scott and Davis (2007).

1970–, Natural models

Open systems models

1960–1970, Rational models

Organizing (Weick, 1979)

Socio-technical Systems (Miller & Rice, 1967)

Bounded Rationality (March & Simon, 1958) Contingency Theory (Lawrence & Lorsch, 1967) Comparative Structure (Woodward, 1965; Pugh et al., 1969; Blau, 1970)

Transaction Cost (Williamson, 1975)

Knowledge-Based (Nonaka & Takeuchi, 1995)

Organizational Ecology (Hannan & Freeman, 1977) Resource Dependence (Pfeffer & Salancik, 1978) Institutional Theory (Selznick, 1949; Meyer & Rowan, 1977; DiMaggio & Powell, 1983)

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Organizational engineering has to be carried out with an understanding of organization as complex systems made up of a wide variety of elements. These elements may be subsumed into two major categories: the natural and the intentional (McKelvey, 1997). The natural are the human, behavioral, action-oriented, and mostly intangible elements that are often problematic to model formally but that cannot be ignored in any attempt to model an organization. The intentional elements are the man-made, rational, planning-driven, and mostly tangible elements that interact with the natural elements in an organization. In line with advances in many of the sciences, McKelvey (1999) argues for a ‘‘model-centered’’ organization science in which research would be bifurcated into two types of activity. On one hand, idealized models of organized or organizing activity would be devised and tested and, on the other hand, descriptive analyses and case studies would be carried out in order to compare ‘‘the isomorphism of the model’s idealized processes/structures with that portion of real-world phenomena’’ (ibid, p. 18). Models do not attempt to explain real-world behavior; they only attempt to explain ‘‘model’’ behavior. In order to make models meaningful and useful to realworld organizations, idealized models must be validated against real-world phenomena. This, in turn, requires a transdisciplinary research disposition. Can autopoietic epistemology inspire and frame a transdisciplinary research disposition? Can it provide the much needed bridge between the hard and the soft sides of the organization sciences? 3.2.4 The Networked Nature of Organizations and Organizing. Increasingly, organizations are being conceptualized as networks with no substance except a system deﬁned by its knowledge (Von Krogh & Roos, 1995b). The knowledge of the organization can also be conceptualized as a network, put together in the following manner (Hanseth, 2004): (1) Individual ‘‘pieces’’ of knowledge are related and interdependent. (2) Different individuals (or actors) adopt the same ‘‘piece’’ of knowledge and that ‘‘piece’’ becomes embedded into organizational routines. (3) As individuals (or actors) and routines are linked together, they become interdependent, and thus begin forming the network. One of the key characteristics of the networking perspective is the interactions that organizational members design and develop in seeking to communicate with other organizational members. Through interactions, organizational members perform socially embedded (i.e., role-based) actions and build relationships at a variety of levels (local, group, intergroup, organization, and interorganization). The relational nature of organizational life and the conception of an organizational member as a social actor are also features of actor-network theory (ANT), an important landmark in contemporary organization theory. As tool for understanding crucial organizational concepts, the network concept can be explained as follows:

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[the network] has the same relationship with the topic at hand as a perspective grid to a traditional single point perspective painting: drawn ﬁrst, the lines might allow one to project a three-dimensional object onto a ﬂat piece of linen; but they are not what is to be painted, only what has allowed the painter to give the impression of depth before they are erased. (Latour, 2005, p. 131)

ANT can be seen as a systematic way to bring out the network infrastructure that is usually omitted in ‘‘heroic’’ accounts of scientiﬁc and technological achievements (Ryder, 2008). ANT views social change as an emergent process that is initiated and guided by actors with speciﬁc interests and strategies and describes the progressive constitution of a network in which both human and nonhuman actors assume identities according to prevailing strategies of interaction. In this sense, organizational life is heterogeneously engineered, after an expression coined by Law (1987). Instead of characterizing the technological world as a neat set of homogenously engineered, cause-and-effect relationships, Law describes it as the result of the activity of myriad dynamic networks, comprised of multiple actors possessing many different attributes, interests, and goals. Besides ANT, other approaches have been put forward that are consistent with the networked view of organizational life. Another relevant example is social capital (Burt, 1997; Nahapiet & Ghoshal, 1998; Adler & Kwon, 2002) which holds that the source of the organizational capacity to create value is the interactions between individuals or between ﬁrms, rather than the individuals themselves. According to this view, knowledge about what products systems produce is becoming less important than knowledge about how systems produce themselves — i.e., how systems renew their own ability and capacity to produce (Zeleny, 2003). Given the considerable degree of convergence between the networked approaches to organization and the tenets of autopoietic theory, can autopoiesis become a more encompassing organizational theory of networks? 3.2.5 The Integration of Social and Technological Architectures in Organizations. Ever since the 1960s, increasing volumes of data have been reduced to text and stored in computers’ memories, making it possible to retrieve, combine, recombine, condense, and transmit data with the greatest of ease. The maturity and pervasiveness of this IT-supported capability raises the issue of the impact of information technologies on organizations to a new level of debate. Managing data has given way to managing information, and the increasing availability of information in organizations is now leading to increasing interest in managing knowledge. Data, information, and knowledge are all concepts relevant to information systems (IS). So, what is an IS? Symons (1991, p. 186/187, emphases added) has suggested the following deﬁnition: a complex social object which results from the embedding of computer systems into an organization (y) where it is not possible to separate the technical from the social factors given the variety of human judgments and actions, inﬂuenced by cultural values, political interests and participants’ particular deﬁnitions of their situations intervening in the implementation of such a system.

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This understanding of ‘‘information system’’ opens up a host of new possibilities for the deployment, use, and management of IT in organizations. In the world of business strategy, there is a general recognition that the ‘‘positions’’ of companies in a competitive market do not assure that they can maintain sustainable advantage. In the ‘‘knowledge economy,’’ both manufacturing and service organizations require an ongoing stream of new capabilities to sustain competitive success. The ability of a company to mobilize and exploit its intangible assets has become far more important than investing in and managing physical, tangible assets (Wernerfelt, 1984, 1995; Conner, 1991; Barney, 1991; Grant, 1991; Mahoney, 1995; Conner & Prahalad, 1996). Intangible assets enable organizations to develop customer relationships, retain the loyalty of existing customers, serve new segments more effectively and efﬁciently, introduce innovative products and services, produce customized products with high quality, low cost, and short lead times, and mobilize employee skills and motivation for continuous improvement. However, following the trend of fragmentation among the sciences in general, organization, information, and systems are commonly treated as independent aspects of organizations that are divorced from each other both in theory and practice. As a result, IS/IT development, implementation, and management are often presented as separate issues from strategic analysis, organizational development, or change management. This discrete treatment of interdependent phenomena places severe limitations not only on the development of multidisciplinary approaches in organization theory, but also on the search for solutions to the practical problems that both management and IS specialists face on a daily basis. In order to move forward, we have to abandon the ‘‘either–or’’ mindset. The problem of integrating IS/IT and the organization cannot be solved by either organization theory or computer science working alone. Similarly, it is pointless to argue about whether organizations are socially engineered or socially constructed. Organizations have to be seen, studied, and managed from both perspectives. We believe that an ‘‘either–or’’ mentality has been a major obstacle to the development of organizational thinking in the 20th and 21st centuries. Magalha˜es (2004) proposes that the problem can only be solved through the adoption of a holistic perspective, founded upon the following assumptions:

(1) Organizations are complex adaptive systems where efﬁcient, effective, and sustainable growth and development depends upon the constant production of new internal knowledge. (2) As in complex adaptive systems, the transformation of organizations often starts from small innovations found at the fringes of the system’s central core of activity. (3) Innovations and new knowledge are partly associated with the implementation and management of IS/IT. (4) IS/IT-related innovations and new knowledge creation are often found at the fringes of the organization’s central core of activity. (5) Organizations need to adopt new managerial theories and practices in order to discover and beneﬁt from the knowledge assets found at their fringes, including new IS/IT-related knowledge (ibid, p. 225).

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In effect, new knowledge assets are to be found in an organization’s social architecture (management systems, structures, performance measures, processes, and culture) as well as in its technical architecture (information and communication infrastructure and applications). These two types of architecture are often considered the pillars of the competitive advantage of companies (Prahalad & Krishnan, 2008), and together these two architectural perspectives are redeﬁning the principles and rules of organizational design and engineering. Hence, the challenge is for new integrated research approaches to be launched in which there is an equal contribution from organization and IT theorists. An appropriate context for such research can be found in the holistic approaches proposed by the complexity framework, including autopoiesis theory and enacted cognition.

4 Conclusion In spite of the growing interest in autopoiesis and autopoietic systems in organizations over the last 10 or 15 years, such interest has not made its way into the textbook domain. In the 1960s, open systems theory, a breakthrough in the biological sciences (Von Bertalanffy, 1950), made its way into the organization sciences through the seminal work of Katz & Kahn (1966) and has held a dominant position ever since. Many of the tenets of open systems now need to be revisited, but so far there has not been an alternative perspective as powerful or inﬂuential in organization theory. Although autopoiesis has been heralded by many as a new systems theory, it has not yet achieved the same kind of impact as open systems thinking, in large part because there is no clear-cut agreement among organization scholars regarding the role or the place of autopoiesis in organization science. Nevertheless, there has been a considerable amount of literature on autopoiesis in organization studies, a selective summary of which has been presented in this chapter. While some authors have adhered to the qualiﬁed approach of Luhmann, others have taken autopoiesis straight from the realm of the biological sciences to organization studies, and have even combined it with other approaches. The result has been a number of proposals, some cautioning observation and interpretation, others supporting analysis and direct intervention, but none attempting to elaborate an integrated or comprehensive approach. In this volume, we have set ourselves the challenge of moving the possibilities for theoretical integration forward and of placing autopoiesis alongside other mainstream approaches in organizational thinking. To this end, ﬁve questions encapsulating the trends at the forefront of contemporary organizational thinking were posed to the authors included in this volume:

(1) Can autopoiesis provide the backdrop for a new organizational paradigm? (2) Framed within the complexity paradigm, can autopoiesis provide the metalanguage for a new theory of organization and management?

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(3) What might be the role of autopoiesis in the turn toward a focus on practice and transdisciplinarity in organizational thinking? (4) How might autopoiesis theory lend further support to the views supporting the networked nature of organizations and organizing? (5) Given its holistic nature, can autopoiesis provide a suitable framework for the integration of IT/IS into social organizations? The invited authors responded in a variety of ways to these questions, and the result is the present volume of original and peer-reviewed papers. The editors hope that the ideas presented here will provide the basis for establishing autopoiesis as an innovative intellectual lever for the study of organizations.

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McKelvey, B. (1999). Complexity theory in organization science: Seizing the promise or becoming a fad?. Emergence, 1(1), 5–32. Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83, 340–363. Miller, E. J., & Rice, A. K. (1967). Systems of organizations. London: Tavistock. Mingers, J. (2001). Information, meaning and communication: An autopoietic approach. In: F. Geyer & J. van der Zouwen (Eds), Sociocybernetics: Complexity, autopoiesis and observation of social systems. Westport, CT: Greenwood Press. Mingers, J. (2002). Can social systems be autopoietic? Assessing Luhmann’s social theory. The Sociological Review, 50(2), 278–299. Mingers, J. (2004). Can social systems be autopoietic? Bhaskar’s and Gidden’s social theories. Journal for the Theory of Social Behavior, 13(4), 403–427. Morgan, G. (1997). Images of organization. Thousand Oaks, CA: Sage. Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital and the organizational advantage. Academy of Management Review, 23(2), 242–266. Nonaka, I., Konno, N., & Toyama, R. (2001). Emergence of Ba. In: I. Nonaka & T. Nishiguchi (Eds), Knowledge Emergence. New York: Oxford University Press. Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. Nye, J. S. (2008). The powers to lead: Soft, hard, and smart. Oxford: Oxford University Press. Orton, J. D., & Weick, K. E. (1990). Loosely coupled systems: A reconceptualization. Academy of Management Review, 15(2), 203–223. Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. New York: Harper & Row. Pondy, L. R., & Mitroff, I. (1979). Beyond open systems models of organization. In: B. M. Staw (Ed.), Research in organizational behavior (Vol. 1). Greenwich, CT: JAI Press. Prahalad, C. K., & Krishnan, M. S. (2008). The new age of innovation. New York: McGraw Hill. Pugh, D. S., Hickson, D. J., & Hinings, C. R. (1969). An empirical taxonomy of structures of work organizations. Administrative Science Quarterly, 14, 115–126. Ryder, M. (2008). Actor-network theory. Available at: http://carbon.cudenver.edu/Bmryder/ itc/act_net.html (accessed 10 January 2008). Sanchez, R. (Ed.) (2001). Knowledge management and organizational competence. Oxford: Oxford University Press. Sanchez, R., & Heene, A. (1996). A systems view of the ﬁrm in competence-based competition. In: R. Sanchez, A. Heene & H. Thomas (Eds), Dynamics of competence-based competition (pp. 39–62). Oxford: Elsevier Pergamon. Sanchez, R., & Heene, A. (2004). The new strategic management: Organization, competition, and competence (textbook). New York, NY: Wiley. Sanchez, R. & A. Heene. (Eds). (2006). Managing knowledge assets and organizational learning, volume 2, research in competence-based management. Oxford: Elsevier Science. Sanchez, R., & Mahoney, J. T. (1996). Modularity, ﬂexibility, and knowledge management in product and organization design. Strategic Management Journal, 17(winter special issue), 63–76. Scott, W. R., & Davis, G. F. (2007). Organizations and organizing: rational, natural and open systems perspectives. Upper Saddle River, NJ: Pearson Education. Selznick, P. (1949). TVA and the grass roots. A study of politics and organization. Berkeley, CA: University of California Press. Selznick, P. (1957). Leadership in administration. New York: Harper & Row.

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Senge, P. (1990). The ﬁfth discipline: The art and practice of the learning organization. London: Century Business. Simon, H. (1997). Administrative behavior. New York: The Free Press. Symons, V. J. (1991). Impacts of information systems: Four perspectives. Information and Software Technology, 33(3), 181–190. Taylor, F. W. (1911). The principles of scientiﬁc management. New York: Harper & Brothers. Trisoglio, A. (1995). Managing complexity. Unpublished working paper presented at the Strategy and Complexity Seminar, London School of Economics, London. Varela, F. J. (1984). Two principles of self-organization. In: H. Ulrich & G. J. B. Probst (Eds), Self organization and management of social systems. New York, NY: Springer Verlag. Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind. Cambridge, MA: MIT Press. Von Bertalanffy, L. (1950). The theory of open systems in physics and biology. In: F. E. Emery (Ed.), Systems thinking. Harmondsworth: Penguin. Von Krogh, G., & Roos, J. (1995a). Organizational epistemology. Basingstoke: Macmillan. Von Krogh, G., & Roos, J. (1995b). Conversation management. European Management Journal, 13(4), 390–394. Weber, M. (1947). The theory of social economic organizations. London: Oxford University Press. Weick, K. E. (1976). Educational organizations as loosely coupled systems. Administrative Science Quarterly, 21, 1–19. Weick, K. E. (1979). The social psychology of organizing. Reading, MA: Addison-Wesley. Wernerfelt, B. (1984). A resource-based view of the ﬁrm. Strategic Management Journal, 5, 171–180. Wernerfelt, B. (1995). The resource-based view of the ﬁrm: Ten years after. Strategic Management Journal, 16, 171–174. Whittington, R. (2006). Completing the practice turn in strategy research. Organization Studies, 27(5), 613–634. Whyte, W. F. (1959). Man and organization. Homewood, IL: Richard D. Irwin. Williamson, O. E. (1975). Markets and hierarchies: Analysis and antitrust implications. New York: Free Press. Woodward, J. (1965). Industrial organization: Theory and practice. New York: Oxford University Press. Zeleny, M. (2003). Knowledge and self-production processes in social systems. In: F. ParraLuna (Ed.), Systems sciences and cybernetics, encyclopedia of life support systems (EOLSS), UNESCO. Oxford: Eolss Publishers. Zeleny, M. (2005). Human systems management: Integrating knowledge, management and systems. Singapore: World Scientiﬁc Publishing. Zeleny, M. (2007). Personal conversation during the 9th International Conference on Enterprise Information Systems, June 12–16, Funchal, Madeira.

PART II PERSPECTIVES ON AUTOPOIESIS THEORY

Chapter 2

Outlining the Terrain of Autopoietic Theory John Brocklesby

1 Introduction ‘‘y hmmm, let’s have a quick look y he opened the suitcase y holy Jesus!, he said y legs!’’ – W.S. Burroughs from Spare Ass Annie and Other Tales

Although Maturana and Varela’s idea of autopoiesis is not new to organization studies — it has hovered around the margins of the ﬁeld for several decades — it has yet to enter the mainstream of organizational thinking. One can debate the reasons for this; however, it is almost certainly the case that the complexity and scope of the idea has been one of the main impediments to its wider take-up. The basic concept of autopoiesis is straightforward enough; it refers to the idea that some systems arise through a circular process in which they ‘‘self-produce’’ their own components. If these components are molecular, the result is a particular class of system that we describe as ‘‘biological’’ or ‘‘living.’’ Beyond this relatively simple idea, for example when the domain of application extends from biological to social systems, or from molecular to abstract components, and when one adds into the mix the broader set of ideas and concepts that people tend to associate with the term autopoiesis, the terrain becomes inﬁnitely more challenging and complex. Here the extended concept, i.e., what is often referred to as ‘‘autopoietic theory (Whitaker, 1996),’’ spans a broad range of topics as diverse as cognition, language, epistemology, emotion, social organizations, culture, human relationships, and ethics, to name but a few. Given this breadth, gaining a sense of what the theory, in its developed form, really is, and where one might best start in trying to understand it, presents a signiﬁcant challenge. This is especially so for those scholars who are accustomed to thinking in reductionist terms and who prefer to develop specialist expertise or understanding in one or two key areas. Even for those who take a more holistic approach, understanding the complete set of ideas at once, in all its ﬁnery, is a daunting prospect. Autopoiesis in Organization Theory and Practice Advanced Series in Management, 29–41 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2010)0000006003

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Maturana and Varela’s distinctive writing style compounds the problem. As John Mingers (1995, p. 2) notes, common words such as ‘‘organization,’’ ‘‘structure,’’ and ‘‘conversation’’ are employed in very uncommon ways, and there is a predilection to communicate ‘‘a density of ideas y expressed with almost mathematical sparseness.’’ Once one becomes accustomed to this style of writing it can be highly effective. Initially though, it makes for hard going, and this does not help in getting the message across. Of the various secondary accounts of autopoiesis that are available, Mingers’ (1995) text has almost certainly had the greatest impact in terms of removing much of the mystique surrounding the area. However, even this work has its limitations. Mingers acknowledges that the breadth and coherence of autopoietic theory is one of its great strengths. Yet his main project was to delve into various controversies surrounding speciﬁc aspects of the theory, such as the question of applying autopoiesis to social systems and organizations, and to evaluate applications within various areas of professional practice such as the law, therapeutic practice, information systems, and artiﬁcial intelligence. Mingers did not speciﬁcally set out to show how the various elements ‘‘ﬁt together’’ in a coherent whole. Hence, gaining a sense of this coherence from this particular source is by no means easy; it requires a degree of cross-referencing and synthesizing that some readers will not have the time or perhaps the inclination to do. Against this background, the following chapter presents an account of what I currently take to be ‘‘autopoietic theory.’’ It is a ‘‘high level’’ view, one that combines the various biological aspects with the derivative human and phenomenological aspects. Arguably the latter present the greatest opportunities for connecting autopoietic theory with issues that are of interest to organization scholars. In order to draw attention to key concepts, these are highlighted, italicized, and presented in uppercase. Thereafter, the chapter presents just enough detail to enable organizational scholars to gain a sense of what the area is all about and, when read in conjunction with the other contributions to this volume, to think about how autopoiesis and autopoietic theory does and might further contribute.

2 A Skeleton Outline of Autopoietic Theory In its developed form, autopoietic theory provides an explanation of what happens when, as biological and social beings, human beings ‘‘observe.’’ That is, when humans make distinctions, utter cognitive statements about their worlds, explain their experiences, and act on these. In formulating his ‘‘THEORY OF THE OBSERVER,’’ Maturana invites us to look to the conditions that interfere with observing to provide a broad clue as to what the key components to such an explanation might be. Thus, he claims that since any interference with either biology or language impedes observing, these are pivotal explanatory components. It is on this basis that ‘‘AUTOPOIETIC THEORY’’ arises as a comprehensive and coherent explanation of what these two elements involve, how they arise, and, importantly, how they interact.

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Drilling down, it arises as an explanation of the nature of (biological) living systems, of how — through social processes — basic language capabilities arise, of how these become more fully developed in living systems that possess a nervous system, and — in human beings — of observing as something that happens ‘‘as a manner of living in language’’ (Maturana, 1993).

2.1

Biology and Physiology

Extrapolating from the basic proposition that biology and language generate the phenomenon of observing, Maturana (see, e.g., Maturana, 1988, p. 63) claims that there are two ‘‘PHENOMENAL DOMAINS’’ involved. Propositions relating to the ﬁrst of these originate in experimental work on color vision carried out in the early 1960s (see Maturana, Lettvin, McCulloch, & Pitts, 1960; Maturana, Uribe, & Frenk, 1968; Maturana, 1970). Subsequently, this led Maturana and Varela to reject the prevailing idea that the nervous system is open to information from the environment, and that human cognition mimics the symbol-based operation of computers. In these experiments (and as an example), it was found to be possible to identify direct correlations between the conﬁguration of external wavelengths of light and activity on the retina of the eye, and between activity in the optic nerve and subjects’ verbal descriptions of color. It was not possible, however, to establish such a link between cellular activity on the retina and activity in the optic nerve. This leads to the proposition that no external stimulus acting on the nervous system can determine an organism’s experience of it. Experience corresponds to neuronal activity; it does not correspond to external perturbations. Thus, whereas popular opinion takes the view that the nervous system is open to environmental inputs, Maturana claims that it is closed, autonomous, and circular. He further claims that the sensory and effector surfaces of the nervous system have a dual character. They operate as elements of the organism and as elements of the nervous system. Acting as components of the organism they operate in the interactions of the organism in its medium, but acting as components of the nervous system they operate in its closed dynamics. This means that the organism as a systemic totality interacts with the medium, but the nervous system as a component part does not. An important consequence of this is the understanding that there is no ‘‘outside’’ to the internal components of the nervous system; there are only internal correlations of neuronal activity. Notions such as ‘‘inside’’ and ‘‘outside’’ require the existence of an observer who can see both and explain changes in one in terms of the other. The nervous system, says Maturana, is an example of a ‘‘COMPLEX UNITY,’’ i.e., an ‘‘entity’’ or ‘‘system’’ that an observer decomposes to focus not just on it, but also on its component parts. All such entities, he suggests, are subject to the principle of ‘‘STRUCTURE DETERMINISM.’’ That is, ‘‘y everything that happens in them happens as a structural change determined y either in the course of their own internal dynamics or triggered but not speciﬁed by the circumstances of their interactions’’ (Maturana, 1990, p. 13).

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Logically, the circularity and closure of the nervous system means that it cannot work with representations. Cognition, therefore, cannot be taken to involve the brain processing symbols that stand for external elements, manipulating representations and then computing a response that is adequate in the light of prevailing circumstances. On this account, although we frequently speak as if we know about the ‘‘real world’’ and can accurately perceive the ‘‘things’’ in it, this is a biological impossibility. Instead of characterizing an object (e.g., ‘‘that tree is green’’) according to what are thought to be its intrinsic characteristics, we do so on the basis of ‘‘what happens to us,’’ through experiences, as the object is ‘‘brought forth.’’ This is the basis for Maturana’s claim that human beings do not exist in the physical space; instead that the physical space exists through human beings. Extending the key notion of circularity, Maturana and Varela sought to relitigate the age-old question of what is meant by the term ‘‘living’’ (Maturana & Varela, 1980). Eventually this led to the claim that all living systems are autopoietic or selfproducing, i.e., they are: ‘‘networks of molecular production such that the molecules produced, through their interaction, generate the network that produced them and specify its extension’’ (Maturana, 1993). Life then is constituted through a dynamic, through a particular closed and circular ‘‘manner of relating’’ of molecules. The term ‘‘AUTOPOIESIS’’ describes what Maturana and Varela describe as the ‘‘ORGANIZATION’’ of living systems. In other words it describes that which is invariant and common to all living systems. When used in this context, ‘‘organization’’ refers to the nature of relationships between components of an entity. As long as these relationships are conserved, the entity maintains its class identity. The ‘‘STRUCTURE’’ of the entity refers to the tangible manifestation of these relationships, i.e., it describes how they appear in phenomena that are perceived to belong to that class identity. In addition whereas class identity is conserved, the concrete manifestation of this can change over time. Thus, when the term autopoiesis is used in the molecular domain, to refer to the ‘‘organization of the living,’’ its structural embodiment across the full range of biological phenomena that we refer to as ‘‘living’’ is subject to inﬁnite diversity and constant change over time. At ﬁrst sight, the idea that living systems are structure-determined, that they have an autonomous autopoietic organization, and, that the nervous system — if there is one — is closed to information from the outside world presents serious difﬁculties in accounting for the manifest adaptability of most living systems. Conventionally, the environment is seen as the primary source of adaptation and change. Thus, in human beings, adaptation is taken to arise as a result of the nervous system’s ability to construct and manipulate environmental representations and, on the basis of these, to act with intent. On this ‘‘COGNITIVIST’’ view, adaptation is very easy to explain. In contrast, as we have already said, autopoietic theory claims that the nervous system is not open to external information; indeed it regards this as biologically impossible. So, if adaptation is not about building representations of the ‘‘outside world’’ and then acting on these, what mechanism is involved? In answering this question, it is helpful to look at what autopoietic theory has to say about the main sources of change in living systems. Essentially there are three of these. Firstly, there is change that arises conditional upon the ﬂow of matter through

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the system (e.g., food, water, air quality, etc.). Secondly, there is change that results from internal dynamics such as metabolic processes and aging. Thirdly — and in the context of this discussion — the most important source of change occurs through a process known as ‘‘STRUCTURAL COUPLING.’’ Maturana and Varela employ this term to refer to situations in which it is possible to observe some sort of ‘‘ﬁt’’ between the living system and the medium. When there are recurrent interactions between an organism and what an observer would regard as its environment, or between one organism and another, structure-determined changes occur in both. In other words, the two structures change congruently, each one according to its own structure determinism. Through this process, the structure of the organism, at any point in time, contains a record of previous interactions. As long as the system survives, i.e., as long as there is conservation of autopoiesis and adaptation, external interactions trigger structural change. Structural coupling, then, is a key mechanism for change in a living system during its lifetime. In human beings, it is also the basis of learning. Whereas cognitivism tends to emphasize the response of the system to its environment, and posits a primarily one-way information ﬂow, the idea of structural coupling implies a much more complex interdependent relationship. It says that when one system perturbs the other, the structure of the perturbed system can change. However, this is not adaptation as cognitivism construes it. Where a change does occur in response to an environmental perturbation, it is because the particular change that occurs is a pre-existing feature, or one possible state, of the system’s structure. The perturbation only triggers it. Turning now to cognition, structural coupling generated by the demands of autopoiesis plays the role that conventional wisdom attributes to having a representation of the world. Traditionally, as I have said, we have come to regard cognition as a process that has the brain manipulating representations of the external world. For organisms that possess a nervous system, this sounds eminently plausible. But what about organisms that are not endowed with a nervous system? How is it that they are often just as well adapted to their medium? And does the absence of a nervous system mean that these organisms are not ‘‘cognitive’’ beings? Of course it is possible to answer the ﬁrst of these questions using cognitivist logic. Thus, an observer might regard physical movement in a single cell unity such as an amoeba — as it surrounds a source of nourishment in its medium — in terms of the amoeba having somehow ‘‘perceived’’ its environment and having ‘‘computed’’ an appropriate response (see Maturana & Varela, 1987, p. 147). Yet this is unsatisfactory since these functions imply the presence of a nervous system and the ability to develop representations which the amoeba clearly cannot do. Despite this, under normal circumstances, the amoeba’s behavior is adequate in its domain of existence; it is adapted to its medium. One could even say that it is a ‘‘cognitive being’’ that has the capacity ‘‘to know’’ albeit not in a conventional humanistic sense. On this account, cognition is inextricably linked to structural coupling which in turn is linked to biology. The physical movement of the amoeba does not involve perception. Instead, there is a process in which changes in the chemical composition

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of the medium trigger changes at the sensory surface of the amoeba. This sets up an internal dynamic that results in the amoeba altering its position relative to whatever in the medium is triggering change. It is this logic that leads to the conclusion that, in its most basic form, cognition is not a mentalistic phenomenon. Rather, it is ‘‘effective action’’ (‘‘COGNITION ¼ EFFECTIVE ACTION’’) in a deﬁned domain of existence (see Maturana & Varela, 1987, p. 29). This process involves the whole organism and is not limited to what might happen in the nervous system or brain if these exist. If an observer sees a unity behaving adequately in some deﬁned domain then we can surmise that it ‘‘knows’’ relative to whatever criteria the observer applies in making the necessary judgment. In this sense cognition equates to the whole process of living. The abstract thinking that goes on in human beings is but a special case of what is a much broader phenomenon (see Varela, Thompson, & Rosch, 1991 for a detailed extension of this argument). In human beings, this linking of cognition with structural coupling provides a distinctive take on the increasingly popular idea that cognition is an integral part of our normal everyday mindful and unmindful activity. According to this line of thought, action that looks like adaptation to an observer, and which the representationalist perspective would explain in mentalistic terms, is merely the system operating in a relationship of structural coupling with a medium. The distinctive contribution of autopoietic theory then is to allege that cognition has to do with the process of living, which, in turn, is inextricably linked to structural coupling. This turns on its head reductionist conceptions of cognition that equate, for example, the mind with the brain and/or limit it to conscious thought. Instead, it asserts that the brain is merely one of many structures through which the mind operates. Such then are the basic distinctions that pertain to the ﬁrst of the two domains that are central to Maturana’s systemic account of observing. To summarize, we can say that living systems are self-producing systems of molecular production which operate in a dynamic structural coupling with a medium. In the case of human beings, the basic biological character of the nervous system is critically important since it makes observing possible, and it has implications for the what and the how of observing in speciﬁc instances. However, it is in the second phenomenological domain, that of relations and interactions with a medium, where observing, and behavior more generally, takes place.

2.2

Relations and Interactions

Biology is clearly necessary and important in understanding how people operate as observers. However, observing is not a biological phenomenon. It takes place in, and belongs to the second phenomenal domain, that of social relations and interactions. Maturana’s explanation of the development of observing in human beings extends the idea of a living system structurally coupled to a medium to that of structural coupling between two or more living systems. It is in the relational space between

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these structurally coupled systems that the key mechanism that allows observing to take place occurs. This process is referred to as ‘‘LANGUAGING.’’ The idea of languaging describes a process in which the fundamental aspect is behavior. It is not, as conventional wisdom would have it, the speech act itself. In order to explain this, it is necessary to revisit the concept of structural coupling. According to Maturana when we observe two or more structurally coupled entities ‘‘in language’’ with each other, what we see is a behavioral process. Using his terminology we see an initial ‘‘coordination of action’’ between the two entities, followed by a further coordination of the two actions. Consider the following: a parent in the company of strangers looks disapprovingly at his/her misbehaving child; the child immediately ceases to misbehave. Alternatively, consider the owner of a dog whose whistle attracts the attention of the dog that brings it ‘‘to heel.’’ In both cases, there is an initial coordination of action: the child’s gaze falls upon the parent and the dog’s falls upon the owner. Next, both the child and the dog do something on the consequences of what is done. In both cases, there is a further coordination of the now already-coordinated actions. This leads to the basic contention that the minimum operation that is involved in language is a ‘‘COORDINATION OF A COORDINATION OF ACTION.’’ On this deﬁnition it follows that a rudimentary form of languaging is possible even in organisms that do not possess a nervous system, and/or in those with a nervous system, but which are incapable of abstract thought in the manner of human beings. Much learned animal behavior such as the courtship, nest building, and chick rearing behavior of birds, or the ‘‘dance’’ of bees, is of this basic type (see Mingers, 1995, p. 78). However, to the extent that there is no abstract thought involved in these activities, and in order to differentiate it from more complex forms of languaging, Maturana refers to this as ‘‘LINGUISTIC CONSENSUAL BEHAVIOR.’’ Where there is an advanced nervous system, the languaging potential of the organism is increased dramatically. In simple terms, the intrusion of a nervous system severs the direct link between sensory and motor surfaces of the organism. That having occurred, although the sensory surface can and does perturb the nervous system, the nervous system begins to interact with itself. Activity within the nervous system thus becomes the object of further activity, which becomes — ad inﬁnitum — the object of further activity and so on. This process (see Mingers, 1995, pp. 73–79, for a fuller description) provides the basis for a massive expansion in the cognitive capability of the nervous system that, in human beings, culminates in abstract thought. Because we human beings have such a capacity, and because we live our lives completely immersed in the full complexity of language, it is hard for us to see that, in its minimum form, languaging involves such a simple act as a coordination of an already-coordinated set of actions. Thus, we tend to describe language in semantic terms, i.e., involving a transmission of information from one organism to another that embodies some meaning. However, when — in a relationship of structural coupling — one organism coordinates its actions with another and there is a further coordination, then we have the basic dynamic that culminates in languaging.

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Neither action is necessarily determined by meaning; rather they are both a consequence of the dynamics of structural coupling. Further recursions of this basic coordination of actions result in language becoming increasingly complex and sophisticated, especially, as I have just said, in organisms such as human beings that are endowed with a sophisticated nervous system. Here, in networks of structural coupling, human beings use words as linguistic distinctions to coordinate actions. Through this process objects become tokens for highly speciﬁc behavioral coordinations. In his classes and public seminars, Maturana uses a number of examples to illustrate this process. The designation ‘‘taxi,’’ for instance, connotes the coordinated and linked actions that are involved in carrying someone from one place to another, in a motor vehicle, in return for the payment of money. The same idea pertains to more abstract entities such as ‘‘justice’’ or ‘‘democracy’’; these too are constituted by and anchored in concrete behaviors. Over time, having arisen through the aforementioned process, the anchoring of objects in actions tends to be forgotten. Thus, ‘‘y objects take place as distinctions of distinctions that obscure the co-ordination of actions that these co-ordinate’’ (Maturana, 1988, p. 47). At this point in time, to all intents and purposes objects and entities become ‘‘in-themselves,’’ which — of course — is how we tend to live them. Once objects have arisen, other developments are possible. We can make distinctions of distinctions and this allows abstract concepts to arise. Meaning then arises as patterns of relationships among descriptions. And once we have objects, we can carry out the operation that allows us to refer to ourselves. Thus, we can become self-aware and self-conscious. Fundamentally, because language is rooted in behavior, which is a relational phenomenon, and because the agreements to symbolize behavioral coordinations through tokens occur in networks of structural coupling, language is a relational phenomenon. Even our so-called ‘‘inner world’’ of solitary reﬂection and consciousness arises determined by our existing structure, which is a record of previous interactions and structural couplings. It could even be said that that which we describe as the mind is a social, not an anatomical or biological, phenomenon. From this perspective, the key point is that language cannot be reduced to speech acts and to abstract mentalistic processes. Although abstract processes are important to human beings, fundamentally language is relational and is anchored in behaviors. Although language might appear to us as being about symbols, fundamentally it is about doing. Entities and objects correspond to ‘‘doings,’’ and language is a ﬂow of coordinations of coordinations of action. And this is a process that arises in our network of structural couplings, i.e., in our relationships with other people. As we coordinate our actions in different ways, as we make new distinctions, and as we come up with new tokens, we are continually weaving linguistic networks with other people. And languaging, says Maturana, is what humans do. It is the total immersion in language, as a manner of living together, that constitutes the human being as the class of living system that it is. Human beings, he claims, are not basically different from other animals, ‘‘y all that is peculiar to us is that we live in conversations’’ (Maturana, 1993b, p. 47).

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In summary, combining the two key components involved in Maturana’s explanation of observing creates the understanding that when human beings observe phenomena they do so as living systems ﬁrst and as languaging beings second. As living systems we are two things: ﬁrstly, we are an aggregation of an inﬁnitely large number of individual autopoietic systems; secondly, we are structure-determined entities that exist in a relationship of structural coupling with a medium. As human beings we are living systems that live our lives totally immersed in the social phenomenon which is language.

2.3

The Relationship between the Two Domains

The precise nature of how these two domains interact is not often acknowledged, or I suspect, fully understood. Key to this is Maturana’s insistence that phenomena must be understood in terms that pertain to it and not in terms that pertain to some other component or domain. This is critically important when it comes to understanding observing and in understanding human behavior more generally. Observing, as we have seen, happens through the mechanism of language. Logically then, since language is a relational phenomenon it cannot be taken to be a process through which the brain manipulates symbols, nor is it reducible to the operation of the nervous system. The same can be said about observing activities that take place ‘‘in language,’’ for example thinking and explaining. These need to be explained in relational terms and not biologically. From this we can further deduce that since ‘‘objects’’ arise in language, notions such as ‘‘self,’’ ‘‘the mind,’’ ‘‘personality,’’ etc. are also relational. As such they arise in, and vary according to the relational circumstances of the moment. They are not ‘‘inscribed’’ in the bodyhood of the individual concerned. Remove the relational context and to all intents and purposes they do not exist. All that is left is a dehumanized biological entity. On this logic ‘‘behavior’’ too is something that arises out of the interaction between the biological entity and the medium. Consider, for example, the nature of the ‘‘behavior’’ or ‘‘act’’ that results from an anatomically generated movement in a particular direction of someone’s arm. This depends wholly upon the medium with which the arm interacts and the prevailing relational circumstances. Identical anatomical movements can generate very different ‘‘behaviors’’ depending upon the physical and social context of its interaction with the medium. Just as a nervous system is necessary for speech acts to take place but language is not explainable simply through reference to the nervous system, so too anatomy and physiology is necessary for ‘‘behavior’’ to occur, but it is the interaction with the medium that makes a behavioral act ‘‘what it is.’’ This illustrates one of the key features of autopoietic theory. Although the biological and relational domains are separate and independent phenomenal domains, they are nonetheless inextricably intertwined as components of a larger systemic whole.

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John Brocklesby y bodyhood and manner of operating as a totality are intrinsically dynamically interlaced; so that none is possible without the other, and both modulate each other in the ﬂow of living. The body becomes according to the manner (in which) the living system operates as a whole, and the manner (in which) the organism operates as a whole depends on the way (in which) the bodyhood operates. (Maturana, 1997, p. 2; see also Maturana, 2003)

It follows then that when someone has any sort of relational encounter — when they reﬂect on an issue (in isolation or with others), when they participate in a conversation, they do so with a built-in predisposition to act in a particular way. The biological structure of the system contains a record of past interactions, and the physiological and emotional predispositions of the moment delimit the range of possibilities. However, what happens during that relational encounter depends upon what happens in the relational context. As a conversation proceeds, as its ﬂow of languaging and emotioning unfolds, people see and do things differently; as someone reﬂects upon an issue their biological processes and mood impact upon what they do. Biology then impacts upon but does not determine what happens in the relational domain. At the same time relational encounters impact upon but do not directly imprint themselves upon biology. At one extreme, an acrimonious exchange between two people may increase the blood pressure of the people concerned. Longer term, if the exchanges are repeated it could trigger cardiovascular disease; one of the participants might even suffer a heart attack and die. At the other extreme, using various meditation techniques and through processes of ‘‘self-talk,’’ someone might learn how to reduce muscular and nervous tension to good long-term effect. In both there is a structural transformation of the system in line with changes in relational circumstances and in the medium itself. Indeed, this is the basis for evolutionary development and learning. However, such changes are always subject to the system’s structure determinism.

2.4

From Biology and Social Relations to Phenomenology and the Derivative Epistemology

The issue here is how the epistemology of autopoietic theory, which again is often handled as a separate discrete topic or aspect, arises out of, and relates, the biological and social components of the work as a whole. Fundamentally, for both Maturana and Varela, ‘‘knowing’’ is a biological issue since, as we have seen, cognition is inextricably tied to the process of living and structural coupling between the organism and the medium. Varela et al.’s (1991) concept of ‘‘ENACTION’’ or ‘‘EMBODIED COGNITION’’ further develops this basic idea. Knowledge, they submit, is constituted in our actions. As an individual confronts new situations various experiences are gained through thinking, sensing, and moving. This means that the way we experience (and ‘‘bring forth’’) the world is very much an active construction involving the whole body. Effective action depends upon having a body with various sensorimotor and orienting capacities that allow an agent to act, perceive, and sense in distinctive ways.

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The ﬁrst and most fundamental point then is that cognition is an integral part of normal everyday activity and extends beyond the brain to encompass the whole body. Cognition, as Mingers (1995) rightly puts it, ‘‘is not detached cogitation, but situated practical action.’’ However, epistemology refers to the grounding and underpinnings of knowledge that, in human beings, emanate from more conventional language-based processes of understanding and explaining the world. For Maturana, what human beings ‘‘know’’ about their worlds depends, moment by moment, upon the precise nature of whatever ‘‘EXPLANATORY DOMAIN’’ is operational for that observer. An explanatory domain is an explicit or, more commonly perhaps, an implicit frame of reference that is deﬁned at the very moment in which an observer distinguishes a particular area of experience, and then explains it. Such domains are constituted through the particular criteria that the observer applies in his/her listening in accepting a generative statement relevant to the area of experience as valid. Because there are as many domains of explanation as there are generative processes and validity criteria, it follows that there is a ‘‘MULTIVERSE’’ of equally legitimate realities. Because the nervous system is closed and circular, and because we are subject to the principle of structure determinism we human beings cannot know in a transcendental sense, and there is therefore no single ‘‘universe.’’ Maturana describes action undertaken in the awareness that an observer only explains experiences and not some independent reality, as the observer placing ‘‘OBJECTIVITY IN PARENTHESES.’’ This ‘‘explanatory path’’ invokes a particular dynamic of interpersonal relations. Statements made in the name of objectivity and ‘‘truth,’’ i.e., in the explanatory path ‘‘OBJECTIVITY WITHOUT PARENTHESES,’’ can easily imply a demand for obedience. Such cognitive statements, says Maturana, ‘‘y are implicit claims of a privileged access to an objective independent reality and are, hence, demands for obedience’’ (Maturana, 1988, p. 61). In contrast, if a speaker gives up on the idea of an independent reality, he or she must take responsibility for statements and present these ‘‘y as invitations to enter in the same domain of reality as the speaker’’ (Maturana, 1988, p. 62), where there is full disclosure of the basic premises and coherences that give the statement validity. Finally, Maturana’s notion of ‘‘CONVERSATION’’ is central in further understanding the linkage between biological and social processes, and people’s explanations and actions. When used in this context, ‘‘conversation’’ refers to a dynamic braiding of ‘‘languaging,’’ which we have already discussed, and what he refers to as ‘‘EMOTIONING.’’ Maturana describes the latter as ‘‘bodily dispositions for actions’’ (Maturana, 1988, p. 42). Importantly, as conversations ﬂow through an interweaving of distinctions and emotions, experiences and explanations alter. While the structure of the observer imposes limits on what is and what is not possible, it is the nature of the conversation and its ﬂow that determines the outcome. As people shift from one emotional state to another, changes take place in what they will and will not do. People behave differently, they see differently, and, importantly, they describe and interpret things differently according to the prevailing emotion. Moreover, languaging and emotioning are braided, each process affecting

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the other. This can be seen in everyday conversations where the speciﬁc distinctions that people use invoke an emotional response and where distinctions used reﬂect the emotion of the moment. Maturana uses the term ‘‘CONSENSUAL DOMAIN’’ to describe these networks of structural coupling that are the site for conversations. In these contexts people learn their emotioning and their languaging with people to whom they are structurally coupled, and, through recurrent interactions, structural patterns become conserved. The picture is further complicated on account of the bodyhood of the observer being at the intersection of not one, but many different conversations within and across different structural couplings. Each one of these has its own braided ﬂow of distinctions and emotions that have been learned through recurrent interactions over time and which alter subject to the dynamic ﬂow of the conversation. This means that just as thoughts and descriptions within a single conversation are subject to change depending upon the ﬂow of the conversation, they can also alter as the observer shifts from one conversation to another.

3 Conclusion In this chapter, I have attempted to provide a broad account of the developed form of autopoietic theory as I currently see it. This account has sought to describe in relatively straightforward terms the various components and aspects of autopoietic theory and demonstrate how these synthesize into a highly distinctive way of thinking that has much to offer scholars from other ﬁelds of inquiry. As I noted at the outset, autopoietic theory has already made some in-roads within the broad territory of organization studies. The aim now is to take this work to another level, to generate more interest and clarity in anticipation that autopoietic theory can play a much stronger role in organization studies than it has to date.

References Maturana, H. (1970). Biology of cognition. In: H. Maturana & F. Varela (Eds), Autopoiesis and cognition. Dordrecht: Reidel. Maturana, H. (1988). Reality: The search for objectivity or the quest for a compelling argument. Irish Journal of Psychology, 9, 25–82. Maturana, H. (1990). Science and daily life: The ontology of scientiﬁc explanations. In: W. Krohn, G. Kuppers & H. Nowotny (Eds), Selforganization: Portrait of a scientiﬁc revolution (pp. 12–35). Dordrecht: Kluwer. Maturana, H. (1991). Response to Jim Birch. Journal of Family Therapy, 13, 375–393. Maturana, H. (1993). Videotaped seminar on language and cognition. Melbourne: St. Kilda. Maturana, H. (1997). Metadesign. Available at: http://www.inteco.cl/articulos/metadesign.htm Maturana, H. (2003). Autopoiesis, structural coupling and cognition: A history of these and other notions in the biology of cognition. Available at: http://www.matriztica.org/1290/ article-28335.html

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Maturana, H., Lettvin, J., McCulloch, S., & Pitts, W. (1960). Anatomy and physiology of vision in the frog. The Journal of General Physiology, 43, 129–175. Maturana, H., Uribe, G., & Frenk, S. (1968). A biological theory of relativistic color coding in the primate retina. Archives of Biological and Medical Experiments, 1, 1–30. Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht: Reidel. Maturana, H., & Varela, F. (1987). The tree of knowledge — The biological roots of human understanding. Boston, MA: Shambala. Mingers, J. (1995). Self-producing systems — Implications and applications of autopoiesis. New York, NY, and London: Plenum Press. Varela, F., Thompson, E., & Rosch, E. (1991). The embodied mind — Cognitive science and human experience. Cambridge, MA: MIT Press. Whitaker, R. (1996). The Observer Web: The Internet Nexus for Autopoiesis and Enaction, available at: http://www.enolagaia.com/AT.html

Chapter 3

Overcoming Autopoiesis: An Enactive Detour on the Way from Life to Society Ezequiel A. Di Paolo

1 Introduction Modern organic metaphors for society have run parallel to the very idea of sociology as a science, starting with Comte and Spencer’s use of the term ‘‘social organism’’ (Comte, 1830–42; Spencer, 1897). These metaphors provide a self-renewing source of debate, analogies, and disanalogies. Processes of social regulation, conservation, growth, and reproduction provoke an irresistible epistemic resonance and make us lose little time in offering explanations resembling those of biological regulation, conservation, growth, and reproduction. The phenomenon has not been restricted to metaphor-hungry social scientists: the ﬁnal chapter of W. B. Cannon’s The wisdom of the body (1932) is called ‘‘Relations of biological and social homeostasis.’’ Attempts to apply a modern theory of living organisms — the theory of autopoiesis (Maturana & Varela, 1980) — to social systems are but the latest installment in this saga. Despite the appeal of the organic metaphor, there are good reasons to remain skeptical of these parallels. ‘‘Because every man is a biped, ﬁfty men are not a centipede,’’ says G. K. Chesterton (1910) ironically in his essay against the medical fallacy. Doctors may disagree on the diagnosis of an illness, he says, but they know what is the state they are trying to restore: that of a healthy organism (implying, admittedly, a rather unproblematic concept of health). In social systems, a ‘‘social illness’’ confronts us with precisely the opposite situation: the disagreement is about what the healthy state should be. In asking about the health of an organism, we ask about its norms, about the logic of integration by which its components are reciprocally means and purposes of a unity, and about how this unity enters into relations with its milieu. The question is: Is such logic applicable to social systems? Georges Canguilhem (who already in 1951

Autopoiesis in Organization Theory and Practice Advanced Series in Management, 43–68 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006004

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used the term autopoetique to deﬁne the character of living organisms, see Canguilhem, 1965) contrasts biological history with the history of societies: The biological evolution of organisms has proceeded by means of stricter integration of organs and functions for contact with the environment and by means of a more autonomous internalization of the conditions of existence of the organism’s components and of what Claude Bernard calls the ‘‘internal environment.’’ Whereas the historical evolution of human societies has consisted in the fact that collectivities less extensive than the species have multiplied and, as it were, spread their means of action in spatial externality and their institutions in administrative externality, adding machines to tools, stocks to reserves, archives to traditions. In society the solution to each new problem of information or regulation is sought in, if not obtained by, the creation of organisms or institutions ‘‘parallel’’ to those whose inadequacy, because of sclerosis and routine, shows up at a given moment. Society must always solve a problem without a solution, that of the convergence of parallel solutions. Faced with this, the living organism establishes itself precisely as the simple realization – if not in all simplicity – of such a convergence. (Canguilhem, 1991/1966, pp. 254–255)

There is something deﬁnitely not organic about human societies; something inherently artiﬁcial, and attempts to cast them into an organic mold seem to entail a return to a nonhuman form of life. We should not ﬁnd it surprising that organic and holistic views of society have fuelled the ideology of totalitarianisms (Harrington, 1996). For this reason, and for the emphasis that the theory of autopoiesis puts on the concept of the boundary as an essential determinant of self-production, one of its founders, Francisco Varela, distanced himself from attempts to directly apply autopoiesis beyond the strictly biological domain (Varela, 1989; Protevi, 2008). The cells in our bodies are the perfect slaves; their successful rebellion ends up killing them and us. As is the fate of attempts to describe the social, the autopoietic perspective has soon acquired a prescriptive character, in many cases explicitly so. However, the contemporary discourse on ﬂexible modes of production, self-organization, and ﬂuidity for the ethical, opportunity-creating, postindustrial organization has little in common with a straightforward application of the idea of autopoiesis. Far from being an eco-friendly, postmodern, almost new-age model for social institutions, autopoiesis is a ruthless concept. Nothing would be better for a strictly autopoietic company than to quickly install its local version of Fordism and promote consumerism, compartmentalizing the activities of replaceable ‘‘components’’ that, as they are devolved to the milieu at the end of the day, can fuel the machinery by consuming its products. The very facts that the nature of the social is sought in nature, that parallel solutions are the incompatible manifestations of different societal norms, that a conception of is readily turned into a conception for, and that the negative implications of a theory could be a source of worry for an intellectual are the facts we need to account for, in other words, the fact that social normativity (and consequently human subjectivity) seems of a different, less cohesive, and more malleable kind than organic normativity. If there were such a thing as plain and simple social autopoiesis, we would never question it. And yet, we witness the patterns of societal reproduction, of quasi-order, and the networks of communications and exchange closing up on themselves, regenerating the very organizing conditions that give rise to them. And there are at any given time,

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but changing with time, societal norms and underlying injunctions that we do not question. Contrary to what it might seem at this point, I do believe that it is possible to advance on an understanding of human social systems by starting from the theory of autopoiesis. However, it requires a detour, not a direct mapping; this paper merely starts along this path. It requires understanding cognition and its relation to life. In recent years, the effort has been increased in attempts to explore the continuity between life and mind by taking seriously the autonomy of the organism and the experience of the cognizer. This approach began in the work of Varela himself and his colleagues (Varela, Thompson, & Rosch, 1991) and was complemented by efforts in different disciplines including neuroscience, biochemistry, philosophy of biology, cognitive science, and artiﬁcial life, leading to what now may be loosely called an enactive approach to cognition (Thompson, 2007). Attempts to ground mind in life have revealed a need to develop the theory of autopoiesis by disclosing important distinctions that remain unthematized in the original literature (the version to which most of the work on social autopoiesis reverts). If nothing else, these distinctions will simply add to the toolbox of systemic concepts that may be deployed to approach social systems. But, more likely, they may introduce changes in the very questions that drive this research. My purpose here will be to expound the potential of developments in embodied and biologically grounded approaches to cognition to drive these changes. It will be an unsatisfactory paper in many respects. Risking, and almost certainly achieving, a degree of unfairness, I will not go into any detailed exegesis of the rich literature on social autopoiesis. This is a sin that my list of references will verify. The reason for this is solidarious with a second shortcoming: I will not, strictly speaking, develop in full the passage from life to human mind and from human minds to the social. To do this with the care it deserves is beyond the scope of this paper (and of this single author). Moreover, the very direction of this ‘‘passage’’ will soon come into question. I will instead present some themes that become apparent (and sometimes recur) when attempting to reach, from the departure point of autopoiesis, relatively more modest but still quite tall targets such as the concepts of normativity, agency, and autonomy. In this way I seek to make a good out of two wrongs. By remaining at the ‘‘lower ends’’ of the history of transitions in life and mind (i.e., by not quite reaching the complex stages of human beings and even less those of human social institutions), I intend to focus on those aspects of a systemic and biologically grounded approach to cognition that move beyond the theory of autopoiesis and yet remain (slightly) neutral with respect to the social autopoiesis debates. The point being that, in my opinion, many such discussions will simply dissolve as we begin to explore how autopoiesis is extended into an enactive theory of cognition.

2 Bare Autopoiesis The theory of autopoiesis is presented as a biology of cognition. Cognitive science, we must recognize, has since its inception in the 1950s been badly in need of theoretical

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grounding. This is not to say that cognitive science has lacked a theory. The computational/representational view of the mind (or cognitivism, according to which cognition is essentially information processing that occurs in the brain) has dominated the ﬁeld since its beginning and has developed to a very sophisticated degree. However, this view has been strongly criticized over the last two decades from different fronts (robotics, phenomenology, cognitive linguistics, and situated artiﬁcial intelligence, to name a few). The general gist of these criticisms is that observing cognitive systems ‘‘in the wild’’ often throws a very different picture to that of cognitivism, one where complex, causally spread processes encompassing the brain, the body, and the environment self-organize in opportunistic ways to produce appropriate performance under tight temporal constraints. These observations do not ﬁt well the computational/representational picture, as they demand a deeper understanding of the autonomy and identity of a cognitive system. However, criticisms of cognitivism have so far remained like an asteroid belt of negativity around a computational gravity pit into which cognitive science keeps falling again and again. This situation has sometimes resulted in the paradoxical adoption of critical terminology, words like embodiment or dynamics, as a makeover for essentially quite classical views of cognition. What is still lacking is a theoretical core that is rich enough (though not necessarily simple or easy to sell) so as to nucleate these criticisms and at the same time offer a novel positive alternative by thematizing the blind-spots of cognitivism into genuine research questions. Hence, what some people have called the enactive approach has, at its most radical (Varela et al., 1991; Thompson, 2007), turned to the theory of autopoiesis as its conceptual nucleus. It soon became clear that planet enaction would not hold together and provide a hospitable surface to migrate into unless a proper look was cast at its autopoietic core. It is a mistake to take the theory of autopoiesis as originally formulated as a ﬁnished theory (a trap that is easy to fall into because of the rather decisive and muscular language with which the theory is presented in the primary literature — it reads as if ‘‘love it or leave it’’ is the only choice). And yet, several researchers have recently come to the realization that, as a theory of life and as theory of cognition, autopoiesis leaves many important questions unanswered. In particular, several essential issues that could serve as a bridge between life and mind (like a proper grounding of teleology and agency) are given scant or null treatment in the primary literature, and questions about important biological phenomena such as health are not even raised. We must break free from the binary choice in order to make progress, and must do so in more than one sense. We must be able to criticize autopoiesis constructively and soften up its edges so as to create a more fertile theory that holds dear to the most radical and richest ideas of the original formulation and at the same time allows for the fact that these ideas do not explain everything and must be elaborated and/or complemented in order to be useful for the study of cognition (and social systems). We must also go beyond binary choices in the very recognition that the phenomena that autopoiesis has problems with are precisely the biological, cognitive, and social phenomena that are best understood not in binary but in graded and comparative terms: values, meaning, norms, pathologies, and temporality.

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In the original formulation autopoiesis refers to an organizational property of living systems: their physical self-production and self-distinction. This is proposed as the deﬁning property of life. Once we observe a system we should in principle be able to ascertain whether it is autopoietic or not. In fact, autopoiesis is also used as the property that determines the identity of the system. The only way in which time enters into the theory is by the notion of conservation (i.e., invariance within a time interval). The system will always undergo structural changes while maintaining the condition of autopoiesis as long as autopoiesis is conserved. Otherwise, it disintegrates (a phrase that recurs in the primary literature). The only reason why this tautological assertion is of any interest is that the very identity of the system is given (although this is still the observer’s choice) through the self-distinction introduced by autopoiesis. However, here the theory starts to rely on intuitions. Since it is not speciﬁed at all in the deﬁnition of an autopoietic system how this conservation of autopoiesis happens, the theory is often complemented by appeals to metaphors. An autopoietic system is like a homeostatic system, whereby the homeostatic variable is its own organization. A close examination of the deﬁnition does not strictly lead to this conclusion. A homeostatic system connotes a notion of active monitoring and reaction to perturbations that challenge the homeostatic variables. This may indeed be the case in certain autopoietic systems. But the deﬁnition does not rule out fortuitous, nonadaptive conservation of autopoiesis the case of a system that, without any compensatory mechanisms, just happens to be in a situation where its selfproduction is unaffected — maybe short-lived, maybe very fragile, but autopoietic nonetheless. This problem, the nonentailment from the set-theoretic notion of conservation to the dynamical notion of homeostasis, underlies many of the problems we ﬁnd when we try to understand interactive and relational phenomena like teleology, temporality, agency, sociality, and normativity (Di Paolo, 2005). These have been the concern of enactive cognition since its inception in the later work of Francisco Varela. Part of this work can be read as attempts to reconcile the set-theoretic logic of autopoietic theory with the gradedness of concepts such as signiﬁcance, norms, and values (e.g., Varela, 1997). His contention was that an autopoietic system, by the very fact that it is autopoietic, casts a veil of signiﬁcance on its world. It distinguishes encounters as good or bad for autopoiesis (Weber & Varela, 2002). I believe this analysis can indeed be made of actual living systems but not from the plain fact that they are autopoietic. Varela’s favorite example was bacteria swimming up a chemical gradient. It is their very organization (as well as their behavior) that points us observers to the fact that sugar is signiﬁcant to these organisms, while other chemicals are neutral or noxious. It also seems as if more of it is better than less. This is not a fact of the matter of the same kind as that describing a chemical reaction. This is relational fact, which is impossible to appreciate unless we have an organism present to whom the effects of the chemical encounter on the processes of self-construction and self-distinction make sense differentially, i.e., as being more or less good or bad. The project is that of a naturalization of values and norms, leading eventually to a naturalization of intentionality. It strikes at a persistent blind-spot in cognitive science: a grounded notion of agency and meaning. However, the proposal cannot be

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easily reconciled with the primary literature on autopoiesis. To make it work, Varela must introduce notions such as ‘‘breakdowns’’ in autopoiesis, which may be major or minor (Varela, 1991), thus running against the conservation doctrine. Intuitive as such a notion sounds, it makes no sense since autopoiesis is an all-or-nothing property. Autopoiesis as such does not come in degrees (Maturana & Varela, 1980, p. 94). Otherwise, all the talk about conservation simply evaporates (can a system conserve only a part of its autopoiesis?). We simply cannot derive from the axioms of autopoiesis that an autopoietic system will attempt to improve a situation that leads to the future loss of autopoiesis. This would break the assumption of structural determinism by linking knowledge about past events, or about what has occurred to others, in conjunction with the system’s current state with a reference to a future condition — a heresy. And yet, this is precisely what being a cognizer is all about. How, then, to reconcile autopoiesis and cognition?

3 Autopoiesis Plus It would seem as if the conservation doctrine of autopoietic theory is at the root of the problem. However, the set-theoretic analysis based on organizational properties (as opposed to only structural ones) is one of the strongest contributions of the theory. To get rid of it by softening the concept of autopoiesis (making it something relative and capable of partial breakdowns) amounts to reverting to a hazy view of living systems as being deﬁned by a list of properties (growth, reproduction, responsiveness, etc.), the very view that autopoietic theory is trying to overcome. Moreover, such a move would do a biologically grounded theory of cognitive systems no favors since differences in cognitive performance or cognitive capability would be too easily married to essentially metabolic differences. The cognitive domain would not be grounded in autopoiesis but reduced to it. This reduction would be unable to provide explanations of how some cognitive engagements could ever ﬁnd or produce meaning in situations that do not immediately affect metabolism and yet may have future consequences for it (such as a predator spotting the snow prints recently left by a prey). Within the terms of the problem we must attempt a solution that will provide the required property for a grounding of cognition that complements bare autopoiesis: a property that should (1) come in degrees, (2) respond differentially to different situations according to their consequences for the organism, (3) sometimes malfunction, (4) obey the axiom of structural determinism, and yet (5) allow the living system to alter its present operations with respect to nonactualized situations. As external observers we can recognize and evaluate structural differences in autopoietic systems that bear on their future continuity. We can make distinctions that result in measurable events and have predictable consequences. We know when a sick organism has a few hours or days to live. We can indeed point to structural breakdowns in its realization of autopoiesis. We also know it is still alive during this

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process. Similarly, we can distinguish organisms living through risky or comfortable situations. Of course, none of these distinctions is perfect or absolute (the sick organism might recover, the risk of a situation might be illusory, or its comfort belied by imminent but unperceivable danger). We as observers may normatively modulate our actions when we make such distinctions. So it seems as if beneath the all-ornothing viability condition given by the conservation doctrine lies a space of graded and qualitative structural differences from which norms may be extracted by the observer; e.g., it is generally better to avoid risky situations than to seek them, health is better than sickness, and so on. What is required therefore is, not for us observers, but for the organism itself to be able to generate such norms while it is still alive and to regulate its operations accordingly within the space of structural options that corresponds to the conservation of autopoietic organization. This is the property of adaptivity. Autopoietic systems exist far from equilibrium and must tolerate the natural entropic trends by remaining energetically and materially open. In other words, they are robust in that they can sustain a certain range of perturbations as well as a certain range of internal structural changes without losing their autopoiesis. These ranges are deﬁned by the organization and current state of the system and are here referred to as its viability set (we can sometimes measure some aspects of this set, for instance, in variables that must be kept within certain bounds, like blood temperature in mammals). If the trajectory of states approaches the boundary of viability and crosses it, the system dies. The viability set is assumed to be of ﬁnite measure, bounded, and possibly time-varying. Robustness implies endurance but not necessarily adaptivity, which is a special manner of being tolerant to challenges by actively monitoring perturbations and compensating for their tendencies. Adaptivity is deﬁned as (after Di Paolo, 2005, p. 438): a system’s capacity, in some circumstances, to regulate its states and its relation to the environment with the result that, if the states are sufﬁciently close to the limits of its viability, 1. tendencies are distinguished and acted upon depending on whether the states will approach or recede from these proximal limits and, as a consequence, 2. tendencies that approach these limits are moved closer to or transformed into tendencies that do not approach them and so future states are prevented from reaching these limits with an outward velocity.

An adaptive autopoietic system is able to operate differentially in (at least some) situations that, were they left to develop without any change, would lead to loss of autopoiesis. Importantly, while this property is perfectly operational, it is not implied in the deﬁnition of autopoiesis. It is an elaboration that allows us to recover the homeostatic interpretation. A breakdown, for the system, is simply the severity of a negative tendency (a tendency of states to approach the proximal limits of viability) distinguished and measured by the amount of regulative resources that it demands to compensate for it with or without plastic restructuring of the system. A breakdown will typically, but not exclusively, be the result of external perturbations, and in

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addition to responding to them, adaptivity may allow organisms the possibility of avoiding some risky situations and seeking preferable ones. Autopoietic systems that are not just robust but also adaptive possess enough operational mechanisms to distinguish the different implications of different, but still viable, paths of encounters with the environment. If sense-making requires the acquisition of ‘‘a valence which is dual at its basis: attraction or rejection, approach or escape’’ (Weber & Varela, 2002, p. 117), a sense-making system requires, apart from the norm given by self-construction, access to how it currently stands against the all-or-nothing barrier given by that norm. An autopoietic system operating as a consequence of contemporaneous states must be able to recognize in those states, and only in them, the tendencies that relate it as a whole to the potential loss of its own viability. It must also be able to act appropriately on those tendencies. This is the basis for a regulation of organismic operation in normative terms, including how the organism regulates as a whole unity its interactions with the environment. Such normative engagement with the virtual consequences of current tendencies is the hallmark of cognition (though as we shall see, not all norms refer back to the logic of metabolism). Adaptivity is the operative concept that allows us to link autopoiesis (or more precisely as we shall see, autonomy) with cognition. Additionally, it also helps us to make explicit several other biological phenomena while remaining within the framework of autopoietic theory. These are more extensively discussed in Di Paolo (2005). For instance, when adaptive mechanisms operate at the physical boundary of an organism so as to regulate its coupling with the environment, we move from structural coupling (essentially a symmetrical concept whereby system and environment inﬂuence each other without loss of viability) to behavior (an asymmetrical concept where the organism originates the regulation of structural coupling). This behavioral regulation allows us to deﬁne certain adaptive autopoietic systems as agents. In addition, adaptivity helps us make better sense of the temporality of living systems. As I said before, in autopoietic theory, time enters only abstractly in the notion of conservation (i.e., invariance during a time interval of unspeciﬁed duration). But because adaptivity is about distinguishing tendencies of change and reverting them under strict time constraints, it opens up a temporal dimension. This dimension may have very different properties depending on the complexity of the adaptive mechanisms involved but, by the very nature of adaptivity, it already implies the properties of minimal granularity (an adaptive response has the structure of an act, it may succeed or fail, but a minimal time span is required for this), directionality (if you were to invert the ﬂow of time, an autopoietic system would still conserve autopoiesis, but an adaptive system would become dysfunctional), rhythmicity (single adaptive events are self-extinguishing, and precarious circumstances lead to their reactivation), and historicity (because adaptivity is not prescriptive, it may result in a neutral drift through equally viable, nonrisky states; this in itself may lead to the progressive inner structuring of these viable conditions so that adaptive mechanisms will act differently as a consequence of the system’s experience). None of these properties can be deduced from bare autopoiesis (Di Paolo, 2005).

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One further important aspect of this emerging picture is that only thanks to adaptivity can we speak of organismic dysfunction, stress, fatigue, maladaptation, and pathology. Autopoiesis in the conservation view is blind to such phenomena since they all occur while the system is still autopoietic, but adaptivity provides a measure for them. Indeed, it is possible to deﬁne these phenomena in terms of failures of adaptivity such as the exhaustion of adaptive resources, malfunction of regulation, loss of adaptive buffering provoking the activation of extreme regulation, disharmonious activation of conﬂicting adaptive mechanisms, and so on. Thus, by re-establishing an adapted state, possibly through the simultaneous repair of adaptive processes and change in the range and kind of acceptable relations with the environment, a successful cure may well redeﬁne rather than simply restore the organism’s own normativity. Health, from this perspective, is very different from a statistical species-speciﬁc correlation of normality, and there are consequently many ways of being healthy (Canguilhem, 1991/1966; Goldstein, 1995/1934).

4 Norms of Life As we have seen, it is possible to expand the conceptual reach of the theory of autopoiesis by introducing the idea of adaptivity. However, we are still far from having explored this new avenue thoroughly enough to fruitfully connect it with social systems. There are still problems with grounding sense-making in adaptive autopoiesis (e.g., what about values that are underdetermined by metabolism? Isn’t the possibility of adaptive dysfunction an indicator of a merely contingent connection between life and cognition?). Addressing these problems implies adopting a wider perspective, one that permits us to thematize the autonomy of the cognitive domain over its metabolic substrate. It is important to understand this move, because a similar one will be needed in the passage to social systems. The ﬁrst point that needs to be addressed is the logic that links an autonomous process of precarious identity generation and the normative, teleological, valueladen relation between this identity and its medium. Adaptive autopoiesis is but one instance (perhaps the most fundamental) where we witness this link at work. By means of analytical and existential arguments, Hans Jonas has explored precisely this logic (Jonas, 1966). The fact that metabolism sustains a dynamic form of identity (not coinciding with its material constitution at any given time except at the time of death) allows an organism to become free. This freedom is expressed in the capability of the organism to engage with its medium in terms of the signiﬁcance of a situation, thus contributing to its continuing dynamical autonomy and even opening up the possibility of novel value-making. However, this freedom is allowed by very strict and speciﬁc material needs. It is a needful freedom. Rather than being paradoxical, this concept of freedom avoids the problem of determinism by operating on the relation of mediation between the self-sustaining identity and the ‘‘target’’ of its cognitive engagements. In that sense, an autonomous process of identity-generation (like autopoiesis) is, as we have seen above, potentially able, thanks to its structure, to

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determine what sort of access it has (if any) to the norms that describe its different modes of viability. This access may be less or more mediated (the difference, say, between reacting with aversion to contact with hot surface vs. planning our movements so as to avoid touching it, both of which are examples of adaptive regulation). Jonas’ contention is that in the history of life, novel forms of increasingly mediated engagements have appeared allowing for more freedom at the cost of more precariousness. A good example, but not the only one, is provided by animality, where a new order of values is found with the arrival of motility and the coemergence of perception, action, and emotion. By putting a distance and a time lapse between the tensions of need and the consummation of satisfaction, the temporality of adaptivity is ‘‘spatialized.’’ Animals can appreciate right now the danger that is impinging on them from a distance. This is the origin of a special relation with the world, that of perception and action, which is charged with internal signiﬁcance, and hence with the development of an emotional dimension (what might have been an inner life of need and satisfaction now becomes rich in possibilities such as fear, desire, apprehension, distension, tiredness, curiosity, etc.). But this comes at a cost of more severe energetic demands (allowing the necessary fast and continuing movement across varying environmental conditions without replenishment for long periods) and novel forms of risk. Jonas identiﬁes other such transitions, for instance, those afforded by a complex visual system or the capacity to make images. It is clear that no intrinsic gain is implied at the metabolic level by expanding the realm of freedom at the cost of increased precariousness. However, he says, ‘‘the survival standard is inadequate for an evaluation of life’’ (Jonas, 1966, p. 106). He goes on: It is one of the paradoxes of life that it employs means which modify the end and themselves become part of it. The feeling animal strives to preserve itself as a feeling, not just a metabolizing entity, i.e., it strives to continue the very activity of feeling: the perceiving animal strives to preserve itself as a perceiving entity – and so on. Without these faculties there would be much less to preserve, and this less of what is to be preserved is the same as the less wherewith it is preserved. (ibid)

Effectively, such transitions inaugurate a domain that feeds back on itself; they imply a new form of life, not just in a metaphorical sense, but in the strict sense of a novel process of identity generation underdetermined by metabolism. But how is this possible? Can we make sense of this in terms of bare autopoiesis? The problem of how to connect the constructive and the interactive aspects of living organization is already inherent in the phrasing of autopoietic theory. This difﬁculty is hard to appreciate (let alone resolve) from within the terms of theory. The problem is the impossibility of crossing the operational and the relational domains. The ﬁrst pertains to the functioning of the autopoietic network so that it constitutes a unity (a composite system), the second to the relations that such a unity enters into in its structural coupling with the environment (e.g., see Maturana, 2002). For all the logical accountancy that this separation into so-called ‘‘non-intersecting domains’’ affords, the authors have not dwelled on the problem that this separation brings, i.e., a systemic analogue to mind–body dualism. In effect, the theory of autopoiesis

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says nothing about how relational interactions and internal compensations are coordinated. They just happen to be or there is no autopoietic system. So, in this speciﬁc interpretation (irreducibility as nonintersection), autopoietic theory is strictly Cartesian in a way that Jonas is trying to avoid.1 The Malebranchean solution, —i.e., a divine intervention to ensure that body and mind, being nonintersecting substances, remain in coordination — is today represented by appeals to evolution, an appeal pregnant in the phrase ‘‘otherwise it disintegrates.’’ Evolution takes care of sieving out those unhappy organisms for which the two domains are uncoordinated. The major problem that is apparent with this solution is that the relation between the two domains is purely contingent. It happens to be like this because it has helped the system survive. This falls short of grounding the cognitive in the systemic. For, without denial of the role of evolution, it is clear that grounding mind in life requires establishing the necessary links between phenomena in these two domains. What an organism is and what it does are not properties external to each other. By contrast, the Jonasian solution is that a transition to a sustained new form of value-making (such as in animality or image-making) modiﬁes the very organizational conditions that made transition possible. It either changes the form of identity generation that sustains the new interactive domain, or indeed it establishes a new form of autonomous identity (the feeling animal, the perceiving animal, etc.). Despite the problem just highlighted with autopoietic theory, the idea of other forms of autonomy in terms of operationally closed dynamics apart from autopoiesis is indeed an acceptable possibility. The theory highlights the operational closure of the nervous system (Maturana & Varela, 1980, p. 127), and Varela has suggested that other domains may possess similar forms of autonomy, albeit not in terms of relations of material production. Such could be the case of conversations and social interactions (Varela, 1979, 1991, 1997). However, what is left unsaid is in what ways can such identities relate to one another. The transformative relations between constructive and interactive aspects of autonomy leading in themselves to a novel form of identity cannot be directly addressed by autopoietic theory. This is simply because a logical barrier is put between the two domains and because an emphasis on conservation of autopoiesis obscures the possibility of a structural becoming of novel forms of organization encompassing both constructive and interactive aspects of the living.

1

The intention behind the distinction between domains is clear: to prevent any attempt at reducing phenomena across domains. Given that one is a domain that is established by the presence of a whole unity and its relations to its environment, there are good systemic reasons to distinguish those relations from constitutive processes that give rise to the unity. To reduce phenomena across these domains is to confuse things, to search for the speed of the car inside its engine. This is a strong point that should be preserved. The problem, however, is introduced by the term ‘‘non-intersecting.’’ This implies strict separability whereas in fact, non-reducibility does not imply isolating the phenomena between domains. In this way, it is indeed possible for explanations in domain A to depend on phenomena in domain B, but not exclusively so; a powerful engine helps us make sense of the speed of the car even if we cannot deduce the latter exclusively from the former. Where we must be careful is in the form that such a dependence takes since any relation across domains will always be a relation of modulation or constraint, and not of determination.

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We can now understand why the transitions in mediacy described by Jonas (and several others not made explicit by him) have an irrevocable character. They are authentic births of new lifeforms. These new lifeforms may relate to the metabolic substrate and other lifeforms in a variety of ways, calling for veritable topology of processes of identity generation (intersecting, embedded, hierarchical, shared, etc.). It is also an open possibility that the dependence on a form of life so much modiﬁes the basic autonomy of metabolism that the higher identity essentially intervenes in the very condition of organizational closure of autopoiesis (at least temporarily in the case of vivipary or indeﬁnitely as in cases of permanent medical intervention). We shall return to the last possibility later. However, a proper treatment of these problems is beyond the scope of this paper. It will be important to remark that, from a systemic perspective, the relation between different self-sustaining processes enabled by a substrate of autopoiesis need not be one of perfect harmony and that, on the contrary, the inherent regulative tendencies of sophisticated processes of identity generation are likely to enter into conﬂict even with basic metabolic values. I have proposed that habits should be seen as such autonomous structures (encompassing partial aspects of the nervous systems, physiological and structural systems of the body, and patterns of behavior and processes in the environment) (Di Paolo, 2003, 2005). And habits, as we know, can be ‘‘bad.’’ The question is that as self-sustaining structures, they are never bad for themselves, but for some other identity (typically, in the case of humans, a combination of the metabolic and socio-linguistic self).

5 The Enactive Approach to Social Cognition I have dwelled on the complex issues that emerge from attempting to answer questions about cognition in connection with autopoietic theory partly in order to demonstrate the difﬁculties inherent in such a task. The warning should be clear: exporting and expanding the concept of autopoiesis is never an easy ride. I will now sketch more speciﬁcally how the enactive approach has been applied to questions in social cognition. For the enactive view, cognition is an ongoing and situated activity shaped by life processes, self-organization dynamics, and the experience of the animate body. This approach is based on the mutually supporting concepts of autonomy, sense-making, embodiment, emergence, and experience (Di Paolo, Rohde, & De Jaegher, forthcoming; Thompson, 2005, 2007). In this perspective, the properties of living and cognitive systems are part of a continuum and relate to each other in mutually constraining ways. What provisionally could be designated as an ontological ordering (from life to mind) ends up overcoming itself into more complex inner relationships through which mind may have a life of its own and constrains the domain of metabolism that gives rise to it (a point that we shall elaborate later). We could expect, in principle, a similar situation if we consider the relations between life, mind, and society. Only recently has an enactive view been proposed to account for general

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aspects of micro-level social interactions. And indeed the general picture partly repeats itself. Of the ﬁve core ideas, the concepts of embodiment and experience have received much attention. I will not discuss them in detail here (see Di Paolo et al., forthcoming and references therein). We deﬁne sense-making as the engagement of a cognitive system with its world in terms of signiﬁcance or value. Action and perception as well as affective processes are forms of sense-making. It is an activity binding affect and cognition together at the very origins of mental life. This is in contrast to the more traditional view of organisms receiving information from their environment in a more or less passive manner and then processing it in the form of internal representations, which are invested with signiﬁcant value only after such processing. Natural cognitive systems do not build ‘‘pictures’’ of their world (accurate or not). They engage in the generation of meaning in what matters to them according to the logic laid by their self-sustaining identity. They enact a world. The notion of sense-making grounds in biological organization a relational and affect-laden process of regulated exchanges between an organism and its environment. Sense-making is connected with the regulatory capacities of an organism, but more generally with the presence of a process of identity generation. This is the idea of autonomy that we adapt from (Varela, 1979) to include a requirement of precariousness (see also Di Paolo, 2009). Accordingly, an autonomous system is deﬁned as a system composed of several processes that actively generate and sustain an identity under precarious circumstances. In this context, to generate an identity is to possess the property of operational closure. This is the property that among the conditions affecting the operation of any constituent process in the system there will always be one or more processes that also belong to the system. And, in addition, every process in the system is a condition for at least one other constituent process, thus forming a network. In other words, there are no processes that are not conditioned by other processes in the network, which does not mean, of course, that external processes cannot also inﬂuence the constituent processes, only that such processes are not part of the operationally closed network as they do not depend on the constituent processes. Similarly, there may be processes that are inﬂuenced by constituent processes but do not themselves condition any of them and are therefore not part of the operationally closed network. In their mutual dependence, the network of processes closes upon itself and deﬁnes a unity that regenerates itself (in the space where these processes occur). Precarious circumstances are those in which isolated constituent processes will tend to run down or extinguish in the absence of the organization of the system in an otherwise equivalent physical situation. In other words, individual constituent processes are not simply conditioned (e.g., modulated, adjusted, modiﬁed, or coupled to other processes), but they also depend for their continuation on the organizational network they sustain; they are enabled by it and would not be able to run isolated. We shall return to a discussion of the concept of autonomy. As we have seen, similar constitutive and interactive properties have been proposed to emerge at different levels of identity-generation, including sensorimotor and neuro-dynamical forms of autonomy (Thompson, 2007; Di Paolo et al., forthcoming; Varela, 1979, 1997).

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The notion of emergence has had a revival over the last three decades with the advent of the sciences of complexity. Beyond the debates about the possibility of ontological emergence (Kim, 1999; Silberstein & McGeever, 1999), there is a pragmatic application of the term that stems from the well-understood phenomenon of self-organization, which has served to remove the air of mystery around emergence in order to bring it back in line with a naturalistic project. Emergence is used to describe the formation of a novel property or process out of the interaction of different existing processes or events (Thompson, 2007; Thompson & Varela, 2001). In enaction, a relatively strong sense of emergence is often implied (in our case, the sense is slightly stronger than Thompson’s). Accordingly, in order to distinguish an emergent process from simply an aggregate of dynamical elements, two things must hold: (1) the emergent process must have its own autonomous identity, and (2) the sustaining of this identity and the interaction between the emergent process and its context must lead to constraints and modulation to the operation of the underlying levels. The ﬁrst property indicates the identiﬁability of the emergent process whose characteristics are enabled but not fully determined by the properties of the component processes. The second property refers to the mutual constraining between emerging and enabling levels (sometimes described as circular or downward causation). Based on these core ideas, an enactive theory of social cognition would be concerned with deﬁning the social in terms of the embodiment of interaction, in terms of shifting and emerging levels of autonomous identity, and in terms of joint sensemaking and its experience. This is in contrast to deﬁning the problem space of the social as the expansion of a very narrow, but dominant, perspective that focuses only on a problem that might be caricaturized as that of ﬁguring out someone else’s intentions; because of the detached manner in which this is supposed to happen, we have called this a Rear Window approach to the social (De Jaegher & Di Paolo, 2007). Many embodied criticisms of cognitivist theories of social cognition still sometimes fall into some version of this individualism (cf., Gallagher, 2001, 2005; Hutto, 2004; Klin, Jones, Schultz, & Volkmar, 2003). This removed cognitive problem belongs indeed to a theory of social understanding, but it has unfairly deﬁned the ﬂavor of most of the ﬁeld at the expense of downplaying the role of more engaged forms of interaction. The ‘‘social,’’ in today’s social cognition, is deﬁned as a matter of degree (it is nothing but a cognitively more complex domain). The enactive perspective approaches the question of social understanding by means of two nontraditional starting moves: ﬁrst, by providing the tools that allow us to recognize the interaction process as establishing in itself an emerging autonomous domain, and second, by specifying how the activity of sense-making is shaped by interaction to the point that its very nature may change to become a joint activity. By these two moves, the door is open for the autonomy of the micro-social, a bridge between social cognition and macro-level social structures. For the ﬁrst move, we borrow the concept of coordination from dynamical systems theory. Coordination is the nonaccidental correlation between the behaviors of two or more systems that are in sustained coupling, or have been coupled in the past, or have been coupled to another, common, system. A correlation is a coherence in the

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behavior of two or more systems over and above what is expected, given what those systems are capable of doing. For instance, observing a lot of people in a main city square, some standing and some walking, is a coherence of behavior. However, it is hardly surprising since we expect people to walk or stand in such situations (as opposed to hover above the ground, which is not possible, or crawl, which is not usual). However, if we found that they are all facing the same direction, this would be a correlation. It is unlikely, though not impossible, that this would be accidental, however, if we were to discover a common source (a giant TV screen) for this correlation, then this is a case of coordination. Of course, coupling itself is often a source of coordination, a well-known fact in physical and biological systems (Kelso, 1995; Kuramoto, 1984; Winfree, 2001). Coordination is to be expected under a variety of circumstances and does not generally require the postulation of a dedicated mechanism underlying it. It is, on the contrary, often quite hard to avoid. For instance, when asking two people to avoid synchronous oscillations while swinging a pendulum with their arms, Schmidt and O’Brien (1997) found that their oscillations were independent (uncoordinated) when not looking at each other, but presented strong tendency to synchronize when they were allowed to look at each other. Such synchrony is a form of absolute coordination: two series of events are perfectly entrained. Relative coordination, in contrast, has a much wider range of possibilities (Kelso, 1995), as there are no such transitions from one strictly coherent state to another. Systems in relative coordination do not entrain perfectly. Instead they show phase attraction, which means that they tend to go near perfect synchrony, and move into and out of the zone that surrounds it. This is a common phenomenon in biology (Haken & Ko¨pchen, 1991). Of course, coordination may be more than entrainment. Many cases of appropriately patterned behavior, such as mirroring, anticipation, imitation, etc., are general forms of coordination according to our deﬁnition. Several researchers in social science have recognized the importance of different forms of coordination for understanding social interaction, e.g., the tradition championed by ﬁgures such as Erving Goffman, Harvey Sacks, and others (see, e.g., Goffman, 1972, 1983; Sacks, 1992; Sacks, Schegloff, & Jefferson, 1974). A whole ﬁeld of study is dedicated to uncovering behavioral coordination in interaction going under different labels such as interaction studies, conversation analysis, and gesture analysis (see Schiffrin, 1994). Similarly, the coregulation of different kinds of social spaces during interaction has been the interest of social science since at least the work of Edward T. Hall (1966) and Adam Kendon (1990). From the enactive perspective, the concept of coordination helps us to understand social interaction as an ongoing process with a space–time structure and organizational properties. In most approaches that care to deﬁne it, social interaction is simply the spatiotemporal coincidence of two agents that inﬂuence each other (e.g., Goffman, 1972, p. 1; Schutz, 1964, p. 23). We must move from this conception toward an understanding of how a history of coordination demarcates the interaction as an identiﬁable pattern with its own role to play in the process of social understanding. In the social domain, patterns of coordination can directly inﬂuence the continuing disposition of the individuals involved to sustain or modify their encounter.

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In this way, what arises in the process of coordination (e.g., gestures, utterances, changes in intonation, etc.) can steer the encounter and facilitate its continuation. The unraveling of these dynamics itself inﬂuences what kinds of coordination are likely to happen. This is due to the fact that the interactors are highly plastic systems that are susceptible to being affected by the history of coordination. When this mutual inﬂuence is in place (from the coordination onto the unfolding of the encounter and from the dynamics of the encounter onto the likelihood to coordinate), we say that we are in the presence of a social interaction. This emergent level is sustained and identiﬁable. In accordance with the core ideas of enaction, the above description is nothing less than that of an emergent and autonomous process. It is, however, typically a ﬂeeting one. Even though normal social encounters, for instance conversations, may only last a few minutes, our point is that during that period they may organize themselves according to the two avenues of inﬂuence just described: the agents sustain the encounter, and the encounter itself inﬂuences the agents and invests them with the role of interactors. The interaction process emerges as an entity when social encounters acquire this operationally closed, precarious organization. It constitutes a level of analysis not reducible to individual behaviors. This perspective bypasses the circularity that arises from preconceiving individuals as ready-made interactors. Individuals coemerge as social agents with the social process. This brings us to the second requirement for calling an interaction properly social. Not only must the process itself enjoy a temporary form of autonomy, but the autonomy of the individuals as interactors must also remain unbroken (even though the interaction may enhance or diminish the scope of individual autonomy). If this were not so, if the autonomy of one of the interactors were destroyed, the process would reduce to the cognitive engagement of the remaining agent with his nonsocial world. The ‘‘other’’ would simply become a tool, an object, or a problem for his individual cognition.2 In (De Jaegher & Di Paolo, 2007), we propose the following deﬁnition of social interaction: Social interaction is the regulated coupling between at least two autonomous agents, where the regulation is aimed at aspects of the coupling itself so that it constitutes an emergent autonomous organization in the domain of relational dynamics, without destroying in the process the autonomy of the agents involved (though the latter’s scope can be augmented or reduced). (p. 493)

The autonomy of a social interaction is best exempliﬁed by a situation where the individual interactors are attempting to stop interacting, but where the interaction self-sustains in spite of this. Such a situation occurs sometimes when two people walk 2 There may be a social motivation and social consequences to the act of destroying someone else’s autonomy, but the act itself does not in itself constitute an case of inter-action. The deﬁnition of social interaction is aimed at capturing the fact that the other is not fully constituted as a cognitive object by my own actions, but sometimes plays along with them, and sometimes not and this is the crux of the problem of social cognition, the shift backwards and forth between these conditions and the unobjectiﬁable character of the other.

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along a narrow corridor in opposite directions. In order to get past each other, they must adopt complementary positions by shifting to the left or to the right. Sometimes they happen to move into mirroring positions at the same time creating a symmetrical coordinated relation. Due to the spatial constraints of the situation, such symmetry favors an ensuing shift into another mirroring position (there are not so many more moves available). Coordinated shifts in position, then, sustain a property of the relational dynamics (symmetry) that all but compels the interactors to keep facing one another, thus remaining in interaction (despite, or rather thanks to, their efforts to escape from the situation). In addition, the interaction promotes individual actions that tend to maintain the symmetrical relation. Coordinated sideways movements conserve symmetry, and symmetry promotes coordinated sideways movements. While it lasts, the interaction shows the organization described above in terms of the mutual inﬂuence between individual actions and relational dynamics. It becomes clear that interaction is not reducible to individual actions or intentions but installs a relational domain with its own properties that constrains and modulates individual behavior. (Anyone who has reluctantly participated in a self-fuelling argument will immediately appreciate the parallels.) An immediate consequence of this perspective is that if the regulation that sustains a social interaction happens through coordination patterns, and if those patterns affect the movements — including utterances — that are the tools of individual sensemaking, then social agents can coordinate their sense-making during interaction, resulting in participatory sense-making: the interactive coordination of intentional activity, whereby new domains of sense-making may appear that were unavailable to each solitary individual. Participatory sense-making describes in fact a qualitative spectrum of involvement, going from the mere modulation of meaning by physical aspects of the interaction (e.g., delays on a video-conferencing line that affect the ﬂuidity of a conversation and might be sometimes interpreted meaningfully) to intentional regulation of activity between interactors (orientation, teaching) and to cases where the proper act of sensemaking is only completed by joint action (leading potentially to the creation of new meaning). We have seen that individual sense-making possesses the structure of a regulative act. There is an intention in this regulation and, if successful, the conditions that gave rise to this act are extinguished. Consider in contrast a simple social act such as the act of giving. It already has a different structure. A single person cannot complete it because it requires acceptance from another. In a study of mother–infant interaction, Fogel (1993) describes a ﬁlmed session between a 1-year-old baby and his mother that captures possibly the baby’s ﬁrst act of giving. The baby extends his arms and holds it relatively stationary only to gently release the object as the mother’s hand approaches. The object is released only as the mother gently pulls it. Assuming for a moment that the infant is the initiator of the act, we realize that he must create an opening by his action that may only be completed by the action of the mother. The giving involves more than orientation of the mother’s sense-making; it involves a request for her not only to orient toward the new situation, but also to create an activity that will bring the act to completion — in other words, to take up

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the invitation for an intention to be shared. This invitation may go unperceived and the act frustrated. But this is not the same as the situation in which the invitation is perceived and declined. The two situations are different from the perspective of the mother, and this difference conﬁrms that an invitation to participate is experienced as a request to create an appropriate closure of a sense-making activity that was not originally hers. To accept this request is to produce the ‘‘other half of the act,’’ bringing it to a successful completion. When we remove the simplifying assumption that the infant intentionally originated the act, we open up the possibility for even richer degrees of participation. The act may then indeed result from a ‘‘coregulation’’ that emanates from previous aspects of the interaction, as Fogel proposes. A certain movement extending the object in the direction of the mother, without yet intending to give it, may now be opportunistically invested with a novel meaning through joint sense-making. Latent intentions become crystallized through the joint activity so that not only the completion of the act is achieved together, but also its initiation. This sketch hardly does justice to the richness of social interaction, but it highlights the novel aspects of the enactive focus. There is no uniﬁed account that can encompass the whole range of social capacities from primary intersubjectivity to the highest reaches of human language and social cognition. The enactive approach has potential to advance on some of these problems. What this approach does ensure, in contrast to noninteractive proposals, is an explicit two-way link between individual and social processes, leaving open the possibility for individual cognitive skills to have dual or even purely social developmental origins. This is a strictly closed avenue for approaches that are not properly interactive. Social skills, under the enactive view, are by deﬁnition relational. Although agents can have different individual potentials for entering into an interaction, this potential is modulated and transformed by actual interactions. This is an implication of having established the autonomy of the interactional domain. At the same time, the social domain remains social as long as individual autonomy is not lost. This already offers a sharp contrast with some attempts to apply the idea of autopoiesis to social systems. This dialogue, the mutual modulation and potential ongoing conﬂict between autonomies, is not typically discussed in such attempts. But it is precisely a focus concern that comes from deﬁning the social domain in enactive terms. Under this view, the individual and social autonomies are not presented as mutually exclusive starting points from a methodological standpoint. An enactive social science is concerned with what goes on at the interface between these different forms of autonomy.

6 Reduplication, Life-Support, Life/Mind The enactive approach to life and mind, as presented above, is only now starting to turn to the multiplicity of social phenomena. Consequently, at this point it can say very little that is of direct relevance to speciﬁc problems in social science and organization theory. What is possible at this stage, however, is to make explicit some general systemic implications of this approach in the hope that they will expand

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and/or complement the dialogue between systemic approaches to life and social science beyond the bare bones of autopoietic theory. This should help prevent sterile debate around what might turn out to be false problems. Interestingly, as we shall see, such reﬂections turn back into a richer understanding of closure and temporality at the cognitive and metabolic levels. It could be said that our approach has proceeded by disclosing some hidden potentialities that remained unexpressed in the theory of autopoiesis: the possibility of adaptive autopoiesis leading to active homeostasis as opposed to passive conservation; the possibility of adaptive regulation of structural coupling leading to agency and a cognitive relation to the world; the possibility of spatializing the intentional temporality of sense-making into action, perception, and emotion; the possibility of emergent and overlapping processes of identity generation; and the possibility of autonomous social interaction and participatory sense-making. Our interpretation of these developments has so far been positive. They are previously unthematized potentialities that do not break with the basic tenets of autopoietic theory. There is, however, another route for theoretical development that moves from these potentialities back to the core of autopoiesis. This route produces a shift of perspective such that autopoietic theory moves from being a theory of (all) the living to being a moment that allows us to grasp the phenomena of life and mind. By this very development, this moment is overcome (aufgehoben in the Hegelian sense) leading to a more encompassing perspective. It is my ﬁrm belief that social systems thinking will beneﬁt much more directly from this sublated view of life than from bare autopoietic theory. The element that is added to our theoretical toolbox by this turn from the higher forms of identity back into the enabling layers of metabolism is the same kind of tool that may be used to dispel much of the discomfort that work on social autopoiesis still provokes due to its lack of a proper analysis of the relations between the social, the cognitive, and the metabolic levels of autonomy. The ‘‘negative’’ development is best examined as a possibility pregnant in the concepts of operational closure, precariousness, and interactive autonomy. This is shown at the simplest level of a single autopoietic organism, but a similar analysis may be repeated in its central points for all the other possibilities described above where we have seen new forms of autonomy emerging, especially for the case of the autonomy of social interactions.

6.1

Reduplication

According to our deﬁnition, operational closure is an ensemble property of a network of processes. The ‘‘network’’ aspect referred to is that deﬁned by the interdependence between these processes (i.e., relations of constraint and parametrical coupling). The system is precarious: the absence of the network of relations leads individual processes to their termination. The network closes up on itself: the relations of conditioning between processes are circular. Thus, the network deﬁnes an identity. An important implication of precariousness is that were the organization to be removed (its closure ‘‘opened-up’’), the resulting system would not be stable. This, we

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must notice, is not an implication of the concept of closure as presented in autopoietic theory. The condition of precariousness is not made explicit. All that the theory says is that if the organization is transformed into a nonautopoietic one, the autopoietic system ceases to exist. Nothing is said about what remains of those processes that were once components of an autopoietic system. Precariousness is the additional condition that if autopoiesis were lost, the processes that acted as its components would also cease. The requirement of precariousness is again an addendum that reduces the wider set of general closure as introduced in the primary literature. It brings in an inherent dynamical element that we have seen is absent in the original formulation. Even though precariousness and adaptivity do not bear a logical relation (it is possible for a precarious autopoietic system not to be adaptive, or for an adaptive one not to be precarious), they are in factual terms solidarious concepts. It is indeed precariousness that installs the conditions upon which adaptivity, for which there would seem otherwise to be scarcely any need, becomes a useful strategy in the history of life. It is, as we have seen, the combination of precarious autonomy and adaptivity that lays the ground for a cognitive relation with the world. Adaptivity is a more sophisticated form of achieving conservation of autopoiesis. The norms of selfconservation are initially duplicated in the mechanisms of adaptive conservation. They pass from being norms available only to an external observer to being norms available to the autopoietic system itself. If we now consider a precarious, adaptive autopoietic system which has also turned into an agent because of its capability to regulate structural coupling, we may ask what is the effect of such regulation on the metabolic, self-constructing substrate that gives rise to it. The effect, at the beginning at least, cannot be other than reduplication. That is, regulation of coupling subserves metabolism and extends its adaptive powers to the boundary conditions of its own operation. But the ends remain the same. To see this more clearly, let us consider the alternatives for the environmental conditions affecting the viability of bare autopoiesis. Since the system is precarious, there are, by deﬁnition, no conditions available to its structural processes by which they would survive on their own in the absence of the closed network (there may have been such conditions in the remote past, but now they have disappeared — this is something to keep in mind in what follows). But once operational closure is in place, the space of possible environmental or boundary conditions is divided into two: viable conditions and inviable conditions. The ﬁrst is the subset of conditions in which the closed system, given its current structure, would remain viable without eliciting a dangerous approach to the boundaries of the viability set — in principle, this situation might be maintained indeﬁnitely; the second is the complementary subset of conditions. It seems obvious that any durable, barely autopoietic system should have access to situations (internal states, dynamical ﬂows) in which the set of viable conditions is not an empty set; otherwise the system cannot be autopoietic as it would inevitably cross the boundary of viability. What happens with the appearance of agency? Adaptive regulation of coupling reveals the structure of the inviable set. The complement of a set of conditions that guarantee viability is not just the set of conditions that simply negate viability (although such ‘‘lethal’’ conditions are included in it). This set includes conditions

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under which the system is inviable in the long term but not necessarily immediately destroyed — in other words, conditions under which the system is in a ‘‘dangerous’’ transient moving toward its boundary of viability. Agency allows the system to cope with a portion of such dangerous conditions (conditions, as we have seen, that are also cognitively evaluated as dangerous by the system itself by this very coping). It does so by regulating structural coupling in such a way that a dangerous condition is not allowed to subsist long enough to lead the system to destruction. A temporal dimension is introduced in the set of environmental conditions. The severity of these conditions becomes a matter of degree, which is ‘‘measured’’ by of the adaptive regulation deployed to cope with them. Sometimes the regulation at the level of agency can be so reliable as to allow quite a durable persistence in dangerous conditions. As an example, consider the water boatman, one of several species of insects able to breathe underwater by trapping air bubbles (plastrons) in the tiny hairs of the abdomen. The bubbles reﬁll with oxygen due to the differences in partial pressure provoked by respiration and potentially can work indeﬁnitely (see, e.g., Thorpe, 1950). They allow the insect to spend time underwater for longer periods thanks to a mediated regulation of environmental coupling (which is nevertheless riskier than normal breathing). The regulation of coupling (agency) takes the form of maintaining an external structure that directly supplements a vital function in an environmental condition that belongs to the unviable subset.

6.2

Life-Support

Up to this point, agency ‘‘plays along’’ with metabolism without fundamentally altering its systemic properties. It reduplicates its operation by extending in the interactional domain the logic of adaptive conservation of viability. Let us now suppose that the structure of the system is allowed to change while conserving its autopoietic organization (as a consequence of codrifting in the conservation of structural coupling). Agency is in principle no obstacle to this process, which is clearly identiﬁed in the original theory. But the distinction of degree in the severity of nonviable conditions that is enabled by agency opens up a radically novel possibility to this process of structural drift. What if the system were to change its structure (while remaining autopoietic) and ﬁnd itself in a situation where all environmental conditions are inviable? Could this system subsist with an empty set of viable external conditions? Thanks to its active regulation of structural coupling, the answer is that this is indeed possible. All that such system needs to do is to activate its interactive regulation in order to move well in time from one dangerous transient into another that gives it further chances of regulative response (for instance, because it is slower or allows the system to renew some of its resources). The operation would soon have to be repeated and the system would be constantly buying time for itself (imagine a water boatman that moves permanently to an underwater environment by ﬁnding the means to renew its external air bubbles). Agency, thus, contains within itself the radical possibility of performing a function of life-support (in the sense given to this term in the medical ﬁeld). The constructive

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and interactive domains do intersect. Precariousness acquires a higher order; not only are the constitutive metabolic processes unable to continue in the absence of the closed network of relations, but the network itself is unable to remain closed in the absence of the interactive regulation that it originally gave rise to. But, following Jonas, so does freedom acquire a higher order. The set of conditions under which life thrives is now extended so that the transformed metabolism/agent is now able to survive situations in which all possible conditions would lead to its destruction if it had remained only metabolism. And, again following Jonas, as the means that modify the end themselves become part of it, interactive life may acquire a closure of its own. It becomes autonomous by self-organizing plasticity and behavior into habits. It also becomes normative, not only by (a) ﬁlling in with its own norms a ‘‘metabolically neutral’’ space of values and conserving a higher form of life that is enabled by metabolism, as we have already suggested (see also Di Paolo, 2005), but also by (b) potentially driving metabolism to depend on this new form of life. For while the possibility of option (a) is always present, the normativity it introduces is only constrained by that of metabolism — this normativity should not contravene the latter’s viability. New norms, in this case, relate to autopoiesis in a contingent way (like a strong preference in terms of taste between two kinds of food of similar nutritional value). In other words, such norms are metabolically indifferent. This case alone would not be sufﬁcient to understand why the Jonasian transitions would be irrevocable. However, in case (b) metabolism itself changes fundamentally due to the possibilities afforded by autonomous agency. Normativity in the interactive domain is now not entirely contingent and will bear an inner relation to the normativity of metabolism (e.g., a preference for a certain taste in food changing metabolism so that it actually becomes more nutritious; you are what you prefer to eat). Agency in this case does more than downward-regulate metabolism; it ‘‘downward-constitutes’’ it. (In the imaginary case of the permanently underwater insect, this could take the form of the development of a metabolic accommodation to other gases dissolved in the water apart from oxygen followed by a subsequent specialization in diet enabled only because of this new metabolic adaptation). So we can appreciate that it is within the potentialities of agency to alter the domain of viability of metabolism so as to allow it to subsist in conditions that would otherwise be inviable. However, this very alteration can potentially allow metabolism to drift into previously inaccessible situations in which all conditions are inviable, as long as it remains under the self-scaffolded life-support of agency. In strict terms, such a system would be alive but not operationally closed in and of itself, i.e., as a composite network of processes whose operation regenerates the network and deﬁnes it as a unity. It is alive inasmuch as these very same conditions still verify. But these conditions obtain thanks to the system’s actions in the relational domain in which the unity as a whole enters. This relational domain becomes not a contextual, enabling (and essentially contingent and external) condition for the conservation of life, but a necessary, active, and operational process ﬂowing from the relations subtended by the whole unity into the constitution of the composite system. In other words, the system is alive and not sensu stricto autopoietic. It functions as a life/mind unity — the self-sustaining structures of the interactive domain (habits)

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become mutual renditions (not just external coordinations) between the psychic and the somatic.

6.3

Life/Mind

We can see that, as suggested above, the development of autopoiesis in enactive theory leads to a view of life and mind in which autopoiesis itself (interpreted in its traditional sense) is divested from its self-sufﬁciency as a deﬁnition of all life. Instead, it becomes an initiating moment in such a deﬁnition to which we must critically return. Not all known forms of life sublate metabolism in the way I have described, but arguably many do. In particular, most animal forms are unable to survive in their habitat without appropriate and highly specialized sensorimotor skills (not to mention their dependence, in many cases, on appropriate social support). Only in situations that never obtain in their habitat (lab situations), would such forms be able to subsist metabolically were they to be even partially deprived from their agential powers (and by this very fact they already become different lifeforms). Life/mind overcomes the self-sufﬁcient, closed logic of metabolic conservation by conserving itself through means that are not purely metabolic. In this way, the remnants of dualism hidden behind the nonintersection of the operational and the relational domains in autopoietic theory are dispelled without rejecting this distinction but by overcoming it and conserving it. The consequences of this sublation of autopoiesis are signiﬁcant (and largely unexplored). One immediate consequence is that cases where life turns into life/mind are those characterized by potential inner conﬂict and interactive restlessness. We immediately are forced to turn to a more dynamic view of life and cognition. A life/mind cannot ever stay quiet and on the spot for too long (the quietness of phenomena like hibernation belies the exertion required in accumulating sufﬁcient energy and durable safe conditions to survive the winter). A life/mind requires a novel economy of effort and strategizing as part of its very essence. It is in effect what we witness in most animal life. And this strategizing leads to a life of decision-making, struggling, and the constant possibility of inner conﬂict (apart from conﬂict with others) and of imbalances between lacks and excesses (now not only as a pathological case, but as a foundational possibility of this mode of being). Eventually, death itself might be inherent to life/mind, although this is at this point speculative.3

3

Isn’t the inevitability of death related to the situation whereby all environmental conditions are inviable? If so, perhaps death comes as a consequence of the sublation of autopoiesis in life/mind. Notice the difference between any autopoietic system that ceases to exist because it faces an inviable condition, but for which the set of viable condition remains not empty (a prokaryote cell might in principle reproduce ad inﬁnitum if the external conditions are maintained stable, but can of course die as soon as a negative intervention is made in its medium or directly on it) and those life/mind systems for which all conditions are inviable and yet subsist thanks to life-support. Maybe the organizational precariousness of such systems puts them in a situation where death is unavoidable since they have no ‘‘stable home’’ except death.

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7 Conclusion The logic of the return from the higher to the lower — the life-support of agency that turns into the agency of life-support; the scaffold that modiﬁes the terrain upon which it is assembled — is generally lacking in systemic approaches to social systems (and up to this point cognitive science has been in no better position). If we open up a channel through which behavior plastically inscribes itself in the body, so then might social interactions and so could social institutions. Once this possibility is understood in systemic terms, some concomitant questions that arise in debates about whether social systems are or not autopoietic (where are the boundaries?, what is a component?) stop making sense. Such questions form part of an ineffectual discourse and inevitably lead back to the dichotomies of structuralist vs. individual actor perspectives on society. Overcoming a parallel dichotomy in the relation between metabolic and agential normativity should help us surmount the opposition between social structures and individual subjectivity. The notion of a transition in identity generation is the key operative concept introduced by the enactive approach. It overlays autopoietic ideas with an inherently dynamical dimension and redeﬁnes it as a biology of transformation, not just conservation. The substrate of the social is not just the space of meaning (communications, exchange) but also its inscription in living agencies, artifacts, and oblique structuring of habits, which can only be uncovered through genealogical as well as systemic analysis. We can envision a systemic approach to social science whose mission would be to reveal an ecology of different social lifeforms and their transitions, conﬂicts, and transformations. Some social lifeforms might be similar to the extracellular matrix in multicellular organisms or bacterial bioﬁlms, an active medium that structures and is structured by the activity of social actors, something resembling Bourdieu’s habitus (Bourdieu, 1990). Such a form is a soft machine with a receding horizon. It makes little sense to characterize it in terms of boundaries, distinctions, and components. The nature of this kind of social lifeform is such that any attempt at a distinction immediately brings forth a wider background of signiﬁcance and processes in relations never closed upon themselves. Other forms are less diffuse. Self-managed businesses (a phenomenon with many manifestations throughout history but that that has peaked in countries like Argentina and Venezuela over the last 10 years, born out of desperate need, strong communal support, and institutional instability or reform) can in many cases be seen as single exemplars of several transitions in emergent identities (with the attendant expansion of both freedom and precariousness). In their history from bankruptcy to proﬁt-making self-management, they undergo a transformation from an externally supported, almost ascriptional identity which at best is able to sustain a relation of self-distinction — an in-itself — through a process of ‘‘de-grammaticalization’’ of labor, a re-signiﬁcation of individual and collective activity, and a devolution of responsibility to workers and the community at large, so as to become a for-itself — a cause. Such entities might indeed be a closer social analogue not to ruthless bacteria, but to some higher form of life/mind.

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What the enactive approach invites us to do is to see in life, mind, and society, not single unifying notions but multiplicities of events, entities, relations, and processes. These are organized, they are not chaotic, hence the value of systems thinking, but they induce a break from the mythology of stability, boundaries, and conservation. By contrast, the enactive approach foregrounds a discourse of transformations, freedom, precariousness, identities, norms, negativity, temporality, sense-making, and re-inscriptions of meaning in matter and bodies — all notions largely absent in autopoietic theory but not incompatible with it.

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Kendon, A. (1990). Conducting interaction: Patterns of behavior in focused encounters. Cambridge: Cambridge University Press. Kim, J. (1999). Making sense of emergence. Philosophical Studies, 95, 3–36. Klin, A., Jones, W., Schultz, R., & Volkmar, F. (2003). The enactive mind, or from actions to cognition: Lessons from autism. Philosophical Transactions of the Royal Society London B Biological Sciences, 358, 345–360. Kuramoto, Y. (1984). Chemical oscillations, waves and turbulence. Berlin: Springer. Maturana, H. (2002). Autopoiesis, structural coupling and cognition: A history of these and other notions in the biology of cognition. Cybernetics and Human Knowing, 9, 5–34. Maturana, H., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht: D. Reidel Publishing. Protevi, J. (2008). Beyond autopoiesis: Inﬂections of emergence and politics in the work of Francisco Varela. In: B. Clarke & M. Hansen (Eds), Emergence and embodiment: Essays in neocybernetics. Durham, NC: Duke University Press. Sacks, H. (1992). Lectures on conversation (Volumes I and II). Oxford: Blackwell. Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50, 696–735. Schiffrin, D. (1994). Approaches to discourse. Oxford: Blackwell. Schmidt, R. C., & O’Brien, B. (1997). Evaluating the dynamics of unintended interpersonal coordination. Ecological Psychology, 9, 189–206. Schutz, A. (1964). Studies in social theory. Collected papers II. The Hague: Nijhoff. Silberstein, M., & McGeever, J. (1999). The search for ontological emergence. The Philosophical Quarterly, 49, 182–200. Spencer, H. (1897). The Principles of Sociology, 3 vols. New York, NY: D. Appleton and Co. Thompson, E. (2005). Sensorimotor subjectivity and the enactive approach to experience. Phenomenology and the Cognitive Sciences, 4, 407–427. Thompson, E. (2007). Mind in life: Biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press. Thorpe, W. H. (1950). Plastron respiration in aquatic insects. Biological Review, 25, 344–390. Varela, F. J. (1979). Principles of biological autonomy. North Holland, New York, NY: Elsevier. Varela, F. J. (1991). Organism: A meshwork of selﬂess selves. In: A. I. Tauber (Ed.), Organism and the origin of the self (pp. 79–107). Dordrecht: Kluwer Academic Publishers. Varela, F. J. (1997). Patterns of life: Intertwining identity and cognition. Brain and Cognition, 34, 72–87. Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press. Weber, A., & Varela, F. J. (2002). Life after Kant: Natural purposes and the autopoietic foundations of biological individuality. Phenomenology and the Cognitive Sciences, 1, 97–125. Winfree, A. T. (2001). The geometry of biological time. London: Springer. Varela, F. J. (1989). Reﬂections on the circulation of concepts between a biology of cognition and systemic family therapy. Family Process, 28, 15–24. Thompson, E., & Varela, F. J. (2001). Radical embodiment: Neural dynamics and consciousness. Trends in Cognitive Sciences, 5(10), 418–425.

Chapter 4

Innovation and Organization: An Overview from the Perspective of Luhmann’s Autopoiesis Tore Bakken, Tor Hernes and Eric Wiik

‘‘Everything could be different’’ and ‘‘almost nothing can I change’’ – Niklas Luhmann

1 Introduction Few words in modern society have become as positively charged as the word innovation. Of course, premodern societies were also innovative in their way. Still, technology, ideas, and organizational forms have changed over time, and it is only in modern society that innovation has become almost mandatory; that is to say, ranked uppermost in society’s value system. ‘‘Be innovative!’’ has become an imperative in modern society. Niklas Luhmann viewed innovative processes (understood as social change or renewal) not from an action-theoretical perspective, i.e., as the result of an intervention into a social system with the structural changes that go with it, but rather from the perspective of self-referential processes of systems where change in structures are interpreted as changes in communicative events (Ereignisse). Innovation can thus be understood as structural changes where systems react to events in the environment with a changed connectivity between communications (Luhmann, 1984, 470ff.). In action-theoretical studies, however (Joas, 1992; Giddens, 1990), it has been argued that Luhmann’s theory of autopoietic systems prevents a satisfactory understanding of the phenomenon of innovation because it operates with a subjectless action concept that cannot explain learning and innovation processes. The acting subjects that provide the occasion for changed expectations are not co-thematized, which leaves the theory indeﬁnite apropos the question why new structures appear.

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According to these arguments, Luhmann’s theory is relevant only to the study of routines and not to innovative processes (cf. Beckert, 1997, p. 347). But can autopoietic systems not be creative and innovative? Or does the biological roots of the concept and notions such as ‘‘structural determinism’’ and ‘‘structural states’’ make it impossible to capture ‘‘the new’’ in the system’s dynamics? The aim of the following discussion is to outline the theory of autopoietic systems, as it pertains to action theory and the understanding of the phenomenon of innovation. This will be elucidated by examining how systems theory combines concepts of (1) the old and the new, (2) the real and the possible, and (3) the redundant and the variable.

2 The Old and the New 2.1

The Concept of Autopoiesis

Autopoietic systems are cognitively oriented systems where ‘‘events’’ appear in the light of a historically arising structure and its state (what Maturana calls ‘‘structure states’’). Experience is therefore always an important concept in autopoietic systems and becomes in a sense a link between the system’s history and its possible future. With the organizationally required conservatism with which a cognitive system operates, an increasing number of possibilities become actualized, even if in the light of complementary experiences they cannot be satisﬁed. This means that the production of the radically new cannot be ruled out, but depends on the selfreferential, autopoietic system’s mode of operation. This two-sidedness is expressed in Varelas’s (1979) deﬁnition of autopoietic systems: An autopoietic system is organized (deﬁned as a unity) as a network of processes of production (transformation and destruction) of components that produces the components that: (1) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produces them: and (2) constitute it (the machine) as a concrete unity in the space in which they exist by specifying the topological domain of its realization as such a network. (p. 13)

According to such a deﬁnition, the new becomes a construction that must be seen as a communicative and socially produced relationship; and not as a ‘‘Ding an Sich.’’ A posited ‘‘mechanics’’ that can explain the new in this sense is more closely connected with the self-referential character of autopoietic systems. It is in this sense that such systems are cognitive systems. Different reality constructions lead to different (new) action and descriptions, which in turn lead to a differentiation process in the treatment of similar objects. Examples are differentiation between the scientiﬁc disciplines but also the transformation of techniques and technologies between different subsystems — e.g., that continuous change in rocket technology and space research depends upon other subsystems developing new materials that can tolerate heat, which then in turn can be transformed back into space research. Such processes can be described as innovation processes.

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In the following discussion, we will attempt to show how social systems, as a result of their organization,1 are conservative systems, while they at the same time produce social changes, e.g., in the form of innovations due to the fact that they are operative constructions. In order to understand the phenomenon of innovation one needs the concept of ‘‘irritations.’’2 Autopoietic systems allow themselves to be ‘‘irritated’’ by their environment, and this produces new combinatory modes, i.e., innovations. It is in this context, as we will see later, that decisions become so important, as they can serve to accelerate the process. Maturana, Varela, and Uribe (1982) have developed a simulation model for cellular autopoiesis that helps explain how an autopoietic system works. In this simulation model (based on biological autopoiesis), they show how the cell is constantly renewed and changed over time. It is the spontaneous production of an autopoietic unit, in other words, that this formalized model will illustrate. Framed in the terminology of Wittgenstein, one might perhaps say that autopoiesis represents a new language play that can serve to distance the problem from ontological questions connected to what nature’s essence is. If we then introduce the predicate ‘‘produce oneself,’’ then it becomes a part of the language play’s grammar (Wittgenstein, 1967) that determines which organisms can be described. Through the lens of autopoiesis, the concept of the new can thus be seen as ﬁrst and foremost a new language: a new language play establishes a new grammar, and thus new ways of seeing things that can open the way for new combinations, i.e., prepare us to think innovatively.3 This enables a new access to reality: a new language play establishes an area for descriptions that are different in relationship from the old; it demarcates a new context for observation so that new phenomena become clear, which under the old way of looking at things were not. This is how the language play can be made into the logical primacy, and not the ontology that stands in the shadow of the language play or is constituted by it. The grammar of the language play thus deﬁnes the space for possible experiences that are described in the language play, such that the language play creates a basis for distinctions, i.e., a basis for that which is described as self-organizing, autopoietic, and so on. In this way, one can say that the limits of 1

According to Maturana and Varela, the notion of ‘‘organization’’ indicates the identity of a unity, that is to say, what kind of class/group it belongs to. The identity of the unity tells us something about the relations between the components/parts which deﬁne the composite unity of that class/group. The notion refers to the relations between the components which have to be invariant in order to maintain the identity of the unity. Luhmann did not apply Maturana/Varela’s biological notions of organization and structure, as he felt they had little relevance for sociology. 2 In Maturana’s language it is called perturbations. 3 In systems theoretical constructivism ‘‘the new’’ is a kind of emergent order: ‘‘Emergence, in its ‘classical’ meaning, refers to the rise of new levels of being (life as opposed to inanimate nature or of spirit as opposed to life), which in no way may be derived or predicted or explained by turning to the properties of an underlying level. This is why they are perceived as ‘unexpected,’ ‘surprising,’ etc. A modern version of emergence is the rise of, through microscopic interactions at a microscopic level, new qualities that cannot be derived (causally explained, formally derived) from the properties of the components, although they consist solely of the interaction between these components’’ (Krohn & Kru¨ppers, 1992, p. 389 own translation).

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experience lie in the grammar of our experience concepts, i.e., in the language play with which we describe our experience. For Luhmann, there is never any question of a direct analogy between social systems and organisms. Instead he takes the roundabout way of ‘‘generalization and respeciﬁcation’’ (Luhmann, 1995). There are thus differences with the autopoiesis concept of Maturana/Varelas in two respects: (1) Luhmann’s non-organic autopoiesis considers the reproductive processes as ‘‘temporalized.’’ While organisms’ autopoiesis operates with elements (e.g., cells’ organization and operation) which timewise remain relatively constant, social autopoiesis as well as the autopoiesis of consciousness are based on units (communication and thoughts) that have the character of ‘‘events’’: as soon as they take place they vanish.4 A problem thus arises with the connectivity of an event, which is solved with the help of meaning. This leads to a further speciﬁcation of the autopoiesis concept. (2) For in structures and processes that are organized according to meaning, external sizes such as system limits and environment also become included in the analysis in a more cogent way (as we shall see, this will have signiﬁcance for the understanding of innovation, which requires precisely that one disturb autopoiesis’s network). Limits and environment presume meaning for the system, which means that such systems can operate internally with the difference between system and environment. Meaning thus makes it possible for co-interpretation of references not only to the system itself, but also to the system’s environment. The observer can now, to a greater degree, choose what should be attached importance to, the system or the environment. Meaning thus gives support to an evolutionary gain that lies in the capacity to make new combinatory possibilities of closure and environment openness in the system. In this sense, Luhmann allows for operationally closed systems to be open in a new way: closure and openness are no longer seen as opposites. The difference between system and environment is applied system internally as a principle for obtaining information. The operation that promotes this identityconstitutive acknowledgement is observation, and within Luhmann’s system theory, this is understood as marking a difference. In their difference-form systems observe themselves as environment. This relationship between simultaneous environment dependence and independence in systems can be described by the terms mode and event. The systems are independent of their environment as regards their special mode of self-government (this is how they acquire their identity), but dependent when it comes to events in the environment perceived as information. But this opening is always relativized: in its observations, the observing system constructs the observed object from its own perspective. Information is therefore always a self-production of the system, and not a fact in the environment that stand independently of observers. This has consequences for the study of innovation processes. Instead of, as for Maturana/Varela, the observer being ‘‘captured’’ by the system (i.e., constituted 4

This distinction is emphasized by Dirk Baecker (2000): ‘‘But Maturana and Varela use the relative static notion of ‘component.’ Luhmann’s notion of event sheds light over a moment’s disappearance, like a reproductive moment to the creation of the situation — and in this way we can leave the sociology’s use of the notions of intention and action.’’ (p. 126)

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within a separate domain isolated from the autopoietic system that can thus exist only in an ad hoc relation to it), the observer generates the system by drawing a distinction. By placing the observer at the starting point, Luhmann opens the system to alternative constructions. The activity itself that produces the system formation can now be seen more openly. The mechanism for closure can now be shifted from how perceptions function (Maturana et al., 1982) to how the systems’ codes function. The autopoiesis concept in Luhmann’s version thus becomes interesting because it provides a means for understanding how the new is produced (Bakken & Hernes, 2003, 2008). At the same time, the concept of autopoiesis makes an interesting contribution to a better understanding of the interplay between the old and the new in innovation processes. Starting with the autopoiesis concept, we can now bring in a more ‘‘playful attitude’’ to the phenomenon of the new. It is no longer necessary to accept a value system that states that everything new is better than that which existed before.5

2.2

The Old and the New

So how does one explain this in sociological terms? We gain little by explaining phenomena in the modern organizational world from the standpoint that there exist inner tendencies in organizations that lead toward more rational, improved states or innovations, and that management’s task is to nurture these tendencies. Sociology, on the other hand, is better off inquiring into the relationship between organization and society. But why the preference for the new in society? We need to go all the way back to the early modern society of the 1600–1700s, or the period after the discovery of the printing press, to ﬁnd a decisive break (cf. Luhmann, 1995). It was then that the old 5

Luhmann (1997) shows that a shift from what to how questions gives a better description of the new. ‘‘The beautiful world is no longer an object of religious admiration, accompanied by the practical problems of ﬁnding ones path. The ways in which the world manifests itself gives rise to the question of how the world came into being and how similar effects can be obtained [erzeugt] y If we know how to produce something, it may be possible, on the basis of this, to vary [our] goals and decide to create unknown phenomena. The ‘neuzeitliche’ science formulated its understanding of nature based on references to methods and experiments; but even the art of governing started with the question of how to obtain dominance [Herrshaft] and remain in positions of power’’ (p. 520). The German cybernetics and philosopher Gotthard Gu¨nther (1980) is also highlighting this, though in a little bit more Hegelian tone: ‘‘In the old classical conception of the world [Weltbild], which, to be sure, was rich in content, but had a total contextural simplicity, nothing truly and really new could come into existence y Nothing (totally) new newness y cannot manifest itself within a given contexture. And since Hegel, right or wrong, interpreted nature as a closed contexture, nature cannot, according to him, generate anything really new within this contexture. The trivial passage from one content to the next will at the most produce subaltern ‘news,’ like the ones we ﬁnd in the changing world of fashion y The genuinely new, which holds up to the philosophical scrutiny, requires a change in content as well as a switch of contexture. Thus, not only the ﬁrst negation is involved, but the second as well’’ (pp. 197–198, vol. 3, own translation). That is to say, the really new demands not only changes in content but also a change in perspective.

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Latin term novus — which basically meant deviation — was temporalized. And thus the new became a time concept in the sense of receiving attention if it deviates from what came before. This marked a break with the old aristocratic society where age and origin was decisive, also for all contemporary orientation. In principle one was expected to know one’s place and all innovation was regarded as deviation. With the growing functional differentiation of the 1600–1700s came a radical change that swept through all the functional subsystems. Within art originality became prized; genius and the ability to create new works were preferred over the cultivation of memory as it applied to known stories and known picture conﬁgurations. Within the sciences the real meaning of research turned toward innovation, and the same was true of all technical development. Education became subject to comprehensive reforms, particularly in Protestant schools, and within the economy all was now geared to new markets where one demonstrated one’s success with new and improved products. While the various functional areas may have different degrees of preference for newness, there is nevertheless a general tendency to prefer the new over the old. To be sure, there still exist reservations toward novelty; throughout the entire 1700s, there was no shortage of warnings against unrest, especially among weak government ofﬁcials. Even so, and often described by sociologists, with an increasing functional differentiation came a shift in orientation from deviation to novelty. The problem of deviation was set up against the normative area in society, especially in the law courts. And by virtue of its positive shift it may change, but until such time as it does, one cannot offend against it without incurring sanctions. The mode of thinking sketched here has long had the support of sociological theory, but mostly in the light of the theory of differentiation understood as the division of labor as Adam Smith prescribes, whereby differentiation operates on the premise that society develops toward constantly increasing rationality, as well as increased production within all sectors, not only in the form of better resource exploitation within the economy, but also in politics in the form of increased democracy, etc. There is a one-sidedness in this way of thinking, and it is to this that Luhmann directs his criticism: the hope of increased rationality as a result of the social division of labor is today no longer tenable (Luhmann, 1995). We need a new conception of functional differentiation, claims Luhmann, and it must be extended to include function-systems’ autonomy in the sense of operative closure and self-regulation. There no longer exists an external authority or an Archimedean point that stands independent of the systems. This is well known from Luhmann’s commentaries and will not be pursued further here. Our point is that when Luhmann speaks of operative closure, this in no way means causal closure. Indeed quite the opposite. The causal interdependencies increase because each functional system is dependent upon the others functioning. In addition, the respective systems’ problems can be exported to other systems. Politics can have problems that it cannot solve politically, whereupon a legal clause is made with the expectation that the constitutional law courts will solve the problem; and often the courts then send the problem back to politics and so on. This is somewhat how the economic system thinks when it comes to ecological regulations: my company cannot

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comply with certain environmental demands voluntarily, and therefore a government regulation must be set in place to ensure fair competition; the ecology cannot be made dependent upon the goodwill of a particular company manager. What is impossible and possible in a particular functional system has a causal effect on the other functional systems. This means — and this is relevant to the question of innovation — that the irritation potential in modern society increases.6 As a consequence there is a steady increase in the combination of closure and causal openness. Here there is always misunderstanding in regard to the concept of autopoiesis, namely that operative closure means at the same time causal isolation. That is to say, that the economic system should be able to stand alone, and not be bothered about what goes on in the family, what happens in politics or the courts, etc. The point is that operative closure makes a precise distinction between operative closure and causal dependence/independence. These points must be held strictly separate from each other if we are to understand which self-irritation and which form of problem-passing is taking place in the society without an absolute authority being able to regulate the use of resources. Under conditions of functional differentiation, operative closure means that in a causal relationship greater independence and greater dependence are made possible simultaneously, precisely because the complexity of society increases. In what way can organizations’ potential for irritations be increased, and how can this affect our understanding of innovations? The advantage of using the concept of irritation is that what is coming cannot be known in advance; that is to say, how the system is to prepare itself for irritations — innovatively or conservatively. This remains an open question. Modern organizations can contribute by changing the ways in which they see innovations, not as preferred ways of changing structures but as irritations where something is done in an agitated way, and it remains an open question what comes out of this. What then is irritation? The concept differentiates itself from information in that while information means a surprise in the same way as irritation, information is more the understanding of a deﬁned surprise. One knows what possibilities one has, or one has a horizon of possible events and makes a selection to judge if this is a hint or a surprise, i.e., information. Irritation, on the other hand, is an undeﬁned surprise that is based on the environment (or more precisely system to system), yet remaining a product of the system itself. When the ﬁre alarm goes off it is not the surroundings that are irritated but the ﬁre brigade. The question poses itself thus: Where is the ﬁre? And how extensive is it? In this way the problem is brought over into categories, and it is then one gets the rule in Bateson’s sense: ‘‘Information is a difference that makes a difference.’’ Organizations have the ability to react to the modern society through speciﬁcation and diversiﬁcation in regard to irritation. In this way irritation is always calculated into the environment. There is a ﬁre out there, not in the ﬁre station, but the job of 6

Cf. Luhmann (1997): ‘‘Accordingly, irritation is a systems state that stimulates continuation of the autopoietic operations of a system. But as a simple irritation it leaves open the question of whether structures must be changed as well; i.e. whether new learning processes will be initiated by additional irritations or whether the system is counting on that the irritations, in time, will disappear by themselves, since they are only a one-time event’’ (p. 790, own translation).

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extinguishing the blaze is effected by internal programs. This happens in much the same way in corporate consultancy. Here also in recent years one has become more open to the use of therapeutic and constructivistic measures where one proceeds from a problem area in the organization that is poorly or cursorily deﬁned — one proceeds from an irritation, so to speak. Even though irritations (and information) are constructed internally, they oscillate constantly between self-reference and heteroreference, i.e., with conceptions that one in the organization cannot call clearly deﬁned or predictable. From this kind of thinking, in regard to the relationship between the new and the old (in an autopoietic sense), one can according to Luhmann (2000) conclude that: The unconditional support of innovation (in its positive sense) should be replaced by a recommendation, given the continuous absorption of uncertainty that takes place anyway, to maintain and nurse the irritability of the organization. On the one hand, this is a paradoxical principle, since irritation means a reestablishment of uncertainty. On the other, paradoxes too are included in this recommendation, since the very intention of communicating paradoxes is to irritate. Thus inventiveness is in no way excluded. Nor has any pre-decision been made as to the extent or radicalness of inventiveness [y] However, irritations are not only rewarding in the form of innovation, it is ﬁrst of all because they provoke a decision, and second because even untried and rejected innovations (‘for which the time is still not ripe’) are stored in the systems memory. (pp. 219–220, own translation)

We can now adopt a more reﬂexive attitude to innovation. In this way Luhmann can dismiss old European descriptions of novelty and innovation as an unambiguous rationalization tool. This is a way of thinking that expresses not only the relationship between the old and the new, but also its kindred problem, namely the relationship between the real and the possible.

3 From A Sense of Reality to A Sense of the Possible When Luhmann was awarded the Hegel prize in 1989, Robert Spaemann made the following characterization in his tribute: Luhmann’s ideal type is not homo oeconomicus, politicus or sociologicus, but rather ‘‘the man without qualities’’ in the way of the great novel by Robert Musil. In his book Musil depicts the consequences a plural social world has upon the individual who becomes a ‘‘plural I,’’ as expressed through the main character Ulrich. Such a social world demands that the individual make a choice. The individual is no longer confronted by a fate-determined social world but by a multiplicity of choices, as if he or she stands before a multiplicity of realities: that which was once perceived as a whole must now be seen as a system of variables. And it is the same fragmentation that the individual is exposed to. In his studies of Musil and The Man without Qualities, sociologist P. L. Berger (1988) has demonstrated what consequences Musil’s position has for the modern subject: It is becoming y increasingly more difﬁcult to regard the ‘‘I’’ as the center of a single individual’s actions. Instead, these actions are now regarded as events, which, without the doing of the individual, befalls him, and which may be explained by either exterior (social) or interior (organic or psychic) causes. The Cartesian ‘‘I,’’ that used to proclaim its ‘‘cogito ergo sum,’’ has been

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dissolved in a Machian stream of thingliness [Dinghaftigkeit]. The modern subjectivity erodes itself, so to speak. (p. 135, own translation)

In The Man without Qualities, Musil depicts the ideological confusion and collapse that characterized the ﬁnal years of the Austro-Hungarian double-monarchy from 1913 to 1914. A lack of substance and overall vision marked the picture of the times, the ofﬁcer and the civil servant are stuck in their ‘‘partial values’’ and have lost their sense of the possible. The novel’s main character — Ulrich, the man without qualities — should not be seen as without character; he is above all the man of possibilities. He is not immobilized in a determined role but, rather, he is open and creative, he sees reality as hypothesis, he lives in the conjunctive, he experiments.7 But if there is such a thing as a sense of reality — and no one will doubt that it has its raison d’eˆtre — then there must also be something that one can call a sense of possibility. Anyone possessing it does not say, for instance: Here this or that has happened, will happen, must happen. He uses his imagination and says: here such and such might, should or ought to happen. And if he is told that something is the way it is, then he thinks: Well, it could probably just as easily be some other way. (Musil, 1979, p. 12, vol. 1)

It is the next step’s ethics that Musil aspires to: an exacting way to be a person. One might also use the term ‘‘self-creating,’’ where the direction is not ﬁxed. Not the strategic, but the tactical is in focus. This sense of the possible, moreover, runs strongly in German romantics like Fichte and Schelling who operate with their indeterminate person who bears the mark of being ‘‘not yet’’; or the art which in a manner of speaking approaches the ‘‘uncreated creating.’’ In the same way as Musil, Luhmann also takes to task all forms of substance thinking. As the central character in Musil’s novel Ulrich says ‘‘The world has not only one meaning, it has uncountable meanings’’ and ‘‘the one binding truth disintegrates into hundreds of relative truths,’’ it is therefore natural that mathematics’ absence of substance becomes the yardstick for Musil’s central character: ‘‘One and the same case has a hundred sides, each side a hundred relationships.’’ In the same way mathematics (especially that of Spencer Brown) takes a central position in Luhmann’s system theory, and from the standpoint of Whitehead and the pragmatists Luhmann also rejects the substance school of thought. When Whitehead in Process and Reality (1978) seeks to understand what ‘‘process’’ is, he rejects Kant’s substance-oriented logic that indeed stands as a special instance of one-sided predicate logic. Process — Whitehead says — can only be understood from ambiguous predicates. An ‘‘event’’ — or that something happens — is without substance, and a subject–predicate logic must therefore be undergone because it is one-sided, i.e., the notion that we are dealing with an unalterable subject that initiates changes must be abandoned. 7

The story tells that Ulrich’s interest for technological innovations almost became an ideological fashion: ‘‘From the ﬁrst moment when he entered the engineering lecture room, Ulrich was feverishly biased. What does one still want with the Apollo Belvedere when one has the new lines of a turbo-dynamo or the smoothly gliding movements of a steam engine’s pistons before one’s eyes?’’ (Musil, 1979, p. 37, vol. 1).

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It is precisely this process aspect that Karl Weick wishes to clarify, and this with a view toward organizations. Like the autopoiesis concept, he seeks to show how organizations operate, and he does this by seeing evolutionary concepts such as variation, selection, and retention as processes. Instead of using the word variation, Weick applies the term enactment8 to describe the active role the organization plays in the production (Gestaltung) of its relevant environment. Organizational processes are thus the distinctive feature of being mutually inﬂuenceable (interdependent) as well as ‘‘loosely coupled.’’9 For multiple realities, in turn, cause loosely coupled systems because individuals share few variables, share weak variables, and differ in their perceptions of covariation among these variables. The existence of multiple realities is not just a byproduct of enactment: it is the major consequence of bounded rationality. (Weick, 1982, p. 385)

With the term enactment one can now describe organizational innovations and their processes in a more satisfactory way because endogenous and exogenous factors can be thematized together. An innovation now becomes understood as an active ‘‘production,’’ i.e., as an organizational activity where the surrounding, partly uncertain environment (e.g., the wants of the customer) gives a ‘‘new’’ Gestalt. With a ‘‘product innovation’’ an organization ‘‘produces’’ its customer base anew. A novelty in the form of an innovation is thus not to be seen as a reaction of an organization to a demand from the environment, but rather an active variation. An enacting organization can choose that which it deems meaningful to continue actively to constitute relevant environments like before, or it can choose to do things differently. It can thus proﬁt an organization to increase the variation by ‘‘producing’’ its environments (one acts differently from before), or by reducing its environment (one acts exactly like before). This is what Weick (1969) calls doing choice: ‘‘Knowing what I know now, should I act differently?’’ (p. 60). This means that relevant environments are called in a different way in an innovation process. Different departments in an organization can thus have a different inﬂuence on this active constitution. The concept ‘‘loose coupling’’ clariﬁes the simultaneous presence of certain, unambiguous, rationally plannable and the more uncertain, ambiguous, non-rationally plannable aspects in relation to innovation processes. The concept points out that many parts of an organization operate simultaneously according to set plans, but that others do not, and this accentuates that these parts can develop a separate identity from the organization. Certain parts of the organization can be described as loosely coupled with other parts, and can thus adjust locally to environments without affecting the rest of the organization. 8 The notion has a strong biological basis and is different from symbolism and ‘‘connectionism’’ because it is a more radical interpretation of the network models: The autonomy of the construction of the system and its order and information is emphasized. It is underlined that the development of complex systems is open where the organisms in a way enact their complementary environment. This point is also made in the theory of autopoietic systems. 9 Cf. Weick (1982): ‘‘Loose coupling exists if (1) A affects B (suddenly rather than continuously), (2) occasionally (rather than constantly), (3) negligibly (rather than signiﬁcantly), (4) indirect (rather than direct), and (5) eventually (rather than immediately)’’ (p. 380).

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Precisely this process aspect, if one re-speciﬁed it for systems in general, is also what Luhmann is at pains to describe. But processes here are to be understood as irreversible events.10 Process is concerned with likely/unlikely events, something which makes the concept sensitive to all forms of risk problematic (cf. Luhmann, 1991). It becomes a way of dealing with complexity: the system can orientate itself to differences and link their operations to them. This is temporalizing of complexity, i.e., the system’s elements cannot base themselves on repetition or routine, but must change according to internal and external demands. It must be prepared for the fact that something else is joining the fray (connection operations). The system can only actualize ‘‘instant’’ connections, and from moment to moment it produces new situations where repetition or change is possible. This is autopoietic systems’ way of functioning: systems of this kind are therefore inherently restless, they are exposed to an ‘‘endogenous’’ dynamic, and they compel themselves constantly to learn structures that are compatible with this. In this respect one can say that autopoietic systems are produced from unstable elements that vary over short periods of time, or indeed that when it comes to events they have no independent duration, but already within their own production vanish. In this way systems with temporalized complexity are continually left to disintegrate. Based on this experience of temporalized complexity, Luhmann made the meaning concept the very basis of his system theory. That which constitutes meaning for Luhmann is not, as for Weber, the signiﬁcance in events, but rather a difference, namely the difference between actuality and possibility. Luhmann thinks that an autopoietic, self-referential system cannot have a stabile basis, and the system’s operations can therefore only be a departure point for further operations. Every operation consists in ‘‘drawing a distinction’’ and calls forth further markings of one side of the distinction. At a particular instant something is indicated (actualized) while at the same time pointing toward other meanings. That which at a given instant in time prevails is thus unstable, it will break off or collapse, and it compels us to select from the domain of the possible something new that can be actualized in the next instant if the autopoiesis is to be continued. Meaning is thus a continual actualization of possibilities. The measure for what is to be done is no longer the perfect, but the possible. At any given moment there is a reference to a horizon or a surplus of further possibilities, which cannot all be actualized at the next moment. Consequently one must select. The connections that are not selected, however, do not disappear, but are maintained as possibilities, i.e., they can be actualized at a later moment. This means that a system can permit errors, go back to the point of departure, and choose another road. The possibilities are after all inexhaustible. In this way the unstable and the restless compel a system into continual self-change, i.e., to become self-innovatory. According to this difference between actuality and possibility one can say with Luhmann that ‘‘everything could be different.’’ But at the same time, no matter how much one chooses, the together-possible (co-possible) will always exceed the togetherrealizable (compatible). The romantic ‘‘everything could be different’’ can quickly

10

This contrary to structures which make time reversible.

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become melancholy resignation: ‘‘and almost nothing can I change’’ (Luhmann, 1971, p. 44).11 But this is the modern risk society’s paradox: the difﬁculty of changing something is due quite simply to the tempo and the dynamics of risk society. The risk society is driven forward at such a high tempo that it has become unchangeable in a way fraught with risk. We know this from organizational theory: no programming of larger changes is in a position to relate to the incalculable complexity the programs are involved in (cf. Luhmann, 2000, 330ff.).

4 The Next Step can Only be Determined through Decision For Luhmann innovation cannot be regarded as a value concept,12 but as a contra-inductive decision-making process y, a decision-making process that decides different from (would otherwise have been) expected, and thereby changes expectations. (Luhmann, 1978, p. 64)

Innovations are possible only to the extent that decision-making processes are able to come up with implementable alternatives. The ﬁrst step toward such a shift is to study organizational changes from an evolutionary theoretical perspective rather than from a planning theoretical perspective. This is not only because an evolutionary theoretical approach operates with a more complex use of concepts, but also because value concepts can be studied in their functional context through such a perspective, i.e., studied on the level of self-descriptions of organizational systems. Value concepts cannot give a complete picture of the actual operations or structures of an organization. With Brunsson (2002) one could say that such concepts organize political modes of speaking, but by no means describe the reality they deal with. For Luhmann (1996) it is a question not only of seeing decisions as a choice from within a range of alternatives, but also to broaden that perspective. In addition one must ask how alternatives are arrived at in a world that is as it is, i.e., how is it possible through a decision to bring about something that was not there before in a world where what happens, happens, and where what does not, does not? Luhmann’s concern consists in relating decision making to the time dimension. This means that we need to assume a difference between past and future, and this at the same time makes a difference between what is past and what is future. The decision causes this difference as a result of the decision to turn out differently from how it would have turned out if one had not taken a decision, it ‘‘causes,’’ or better: the change of the difference is attributed to the decision, however, much the actual inscrutabilities and the complex causal relationships race by. The decision thus makes itself visible by attributing to itself, and in this way past and present can be treated equally. States that are as they are, or which are becoming that which they are becoming to be, break 11

This back and forth between a romantic sense of possibilities and a more skeptical orientation is typical in Luhmann’s authorship. 12 Values are contrafactic stabilized expectations. According to Luhmann they are (1973, p. 40) not very fruitful in modern organizations. Values prevent us from grasping the cognitive aspects in organizations.

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up into differences. And this makes possible a reentry of time into time.13 The past and future time horizons thus become seen in relation to each other and therefore integrated. It matters not in the slightest that a decision cannot change the past or determine the future. And despite this there begins, thanks to the reentry of time into time with every decision, a new history. The future thus for us becomes unknown. But the future’s unknowability is at the same time the greatest resource for the decision, and indeed a precondition for organizations as such, for decisions base themselves on the fact that no one knows what the future holds. One can set goals because one does not know how things will look in relation to what the future will bring. Of course we can come up with some relatively stable assumptions; e.g., that the Oslo fjord will continue to exist. But the existence or nonexistence of the Oslo fjord is not really an area for decisions. Although when one plans a tunnel under the Oslo fjord one produces a niche for the future’s unknowability, and again thanks to the reentry of time into time one is able certainly to take decisions. The more the society is prepared to recognize niches of this kind, the more obvious it will become in the future that decisions become more transitory. But when with these decisions there always begins a new history, the decision perspectives intensify the unknown aspect of decisions. As opposed to natural and cultural philosophers since Bacon and Vico, history is just as fully unpredictable because it is created by people. And it is on the cards that this happens not only through information, but also through imagination. Decisions make themselves dependent on surprises because they are surprises. They can change expectations by being contra-inductive — i.e., innovative. Again: ‘‘Everything could be different’’ and ‘‘almost nothing can I change.’’ This paradox is typical for organizational systems, for organizational systems show more than other systems this two-sidedness of redundancy and variation (cf. Ahlemeyer, 1997; Ashby, 1956; Bateson, 1972).

5 Redundancy and Variation 5.1

Organization as Redundancy

Organizations reduce contingency by creating redundancy. With Wittgenstein (1967) one can say that symbolic generalizations (cf. Parsons and Luhmann) can be understood as ‘‘rules’’ because a rule is followed not only once: To obey a rule, to make a report, to give an order, to play a game of chess, are customs (uses, institutions). To understand a sentence means to understand a language. To understand a language means to be master of a technique. (y199, p. 81)

With Luhmann (1997) one can say that redundancy is to be understood as ‘‘the repetition of the same in other situations’’ (p. 791). The hallmark of organizations as opposed to other social systems is membership, i.e., access and egress. 13

The notion of reentry (Spencer Brown) is based on the semantics of deparadoxation.

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Through membership it is possible to separate the system’s behavioral requirements from members’ behavioral motives, at the same time as those with the help of the organizational mechanism can be linked together in a relatively lasting way. With the help of membership rules organizations succeed, unlike functional systems, to produce speciﬁc modes of behavior ‘‘The soldiers march, the clerks ﬁle, the ministers rule — irrespective of whether it suits them in a given situation’’ (Luhmann, 1975, p. 12, own translation). Organizations are set up for problem solutions of a relatively lasting nature. It is this that Paul Watzlawick (1985) has called ‘‘more of the same,’’ and which is to be seen as the tendency of organizational systems to defend their identity by opposing changes in structure and rules. Even when one encounters maximal disturbance organizational systems tend to avoid utilization of counterproductive formulas (innovative solutions) in favor of utilizing ‘‘more of the same.’’ The hypothesis is that because of their distinctive system typology organizations tend to resist changes. The problem solutions are set up toward the future because historically they have been successful. In Luhmann’s language use organizations are autopoietic in the sense that they recursively produce and reproduce the decisions they consist of. These decisions have the character of being ‘‘events,’’ and as always this means that as soon as they appear they disappear. As decisions continually vanish, they allow themselves to a small degree to change, and provide only the occasion for the production of new decisions, for which the same conditions apply. It is perhaps not so strange then that organizations have problems with learning and change, ﬂexibility and innovations, precisely because they have so many built-in defense mechanisms to counter the new. We see this every day in government administration, large businesses, hospitals, political parties, universities, etc. It is in this sense that one can say that innovations in organizations, from a structural standpoint, are unlikely. Therefore, ‘‘no system exists in an entropic state of complete indeterminacy of the following moment’’ (Luhmann, 1988, p. 172, own translation). This also applies for systems with high structural complexity. In their decision making organizations orient themselves from structures that have already been decided. Such structures are what one might call ‘‘non-problematical decision premises,’’ and have the function of safeguarding and easing the constant stream of decision making (Luhmann, 2000, p. 224).14 If employees at all times were to remain up to speed on everything new, this would, e.g., impair their ability to orient themselves in the market in a more regular manner, which of course is essential if an organization is to survive. The most important forms for such decision premises are decision programs (goal and conditional programs), the laying down of communication channels, job positions, etc. 14

Cf. Luhmann (2000): ‘‘But they [the decision premises] focalize the communication on the differences that have been established in the decision premises. This makes it probable that one will observe future decisions with regard to these preset premises, from a viewpoint of consideration/non-consideration and conformity/ deviance, rather than unfolding the complete complexity of the situation anew every single time’’ (p. 224, own translation).

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In this respect system structures can be seen as condensations of successful problem solutions, and there is therefore no reason not to have ‘‘more of the same.’’ Hence, when organizations meet new problems they will very often attempt to solve them with existing problem-solving programs. When antennas are set up for program execution in organizations, critical, program-changing information is perceived ﬁrst and foremost as disturbance-producing and of no connective importance. This refers to an important difference in organizations that are built up hierarchically: there is a difference between decisions that are taken within the framework of the decision premises and decisions about the decision premises. Employees in touch with the market and customers are disposed toward carrying out the programs and not changing them. Structure-critical information often fails to reach levels in the organization that can take decisions over structure changes. In this respect the term redundancy can describe why innovations are unlikely.

5.2

Organization as Variation

Even so, we know that organizations innovate. When organizations are regarded as social systems with recursive decision production, it means not merely programming, routine and selective rigidity, but it also accentuates changes in such decision premises. Programs can be changed; expertise and communication channels can be worked out anew; job positions can be redeﬁned or done away with, etc. The innovation rate in the modern economy is intense when one thinks of new products and the increasing production diversity (Baecker, 2007). Indeed, today the supply rate has increased so markedly that demand cannot keep pace with it. US studies show that customers can no longer tell the difference between new and the old cars when they attend motor shows. In this way the time one has to adapt to new products grows shorter and shorter. The same is true for computers. Too much re-adaptation leads to time loss for computer users. Experience and familiarity are also beneﬁts. This is why big companies have become so concerned with ‘‘compatibility’’ between product generations. New concepts must to a large degree base themselves on the old, and in this way one meets a problem in ‘‘pre-announcement.’’ Trade fairs not only exhibit new products, but also announce the products for the next generation that will contain all the improvements not yet incorporated into the new products being launched. In this way novelty is imagined into the future, something that can have unfortunate consequences: this form of temporalizing leads to the collapse of market mechanisms through the distortion of purchasing decisions.15 This dynamic stems not ﬁrst and foremost from the organizational systems but rather from the functional systems, especially the economic functional system that is set up for the feedback mechanism proﬁt and loss, and where the market through the 15 It is this kind of problem which has forced Japanese trade authorities to require the producers to lengthen the production cycles among the chip producers, and in this way lower the speed of innovation. But of course, this demands institutional regulations (more distinct rules of the game). But this is not easy to gain.

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prices becomes a highly effective mechanism for reducing the complexity when it comes to resources, motivation, needs, decisions, and products for a manageable format. The market forms an environment that provides effective mechanisms for evaluating results and decisions and for learning from mistakes. Organizations see and calculate connections for their own operations and utilize the complexity of the environment to transform uncertainty into risk in the form of investment risk, purchasing risk, payment risk, etc.16 This is also expressed internally in organizations through change projects running concurrently, which every day challenge secure structures and demand new orientations. This holds out the prospect of an exchange of patterns and structures that compels organizational members to accept more and more of the new. But how can this be understood through system theory terms? To understand innovations in organizations it is above all important to connect with evolution theory. For Luhmann (1997), the different components of evolution (variation, selection, re-stabilization) become linked to the different components of autopoiesis (p. 413ff.). The term variation takes in ‘‘coincidence,’’ ‘‘innovation,’’ and ‘‘enactment’’; selection refers to ‘‘elimination,’’ ‘‘adaptation,’’ and ‘‘rebuilding’’; and re-stabilization encompasses ‘‘reproduction’’ and ‘‘institutionalization.’’ Evolutionary variation challenges the action theorist’s conception that change is linked to the actions of strongly motivated individuals. Variation for Luhmann means that the elements within a system vary, i.e., it is the communications that vary. Variation consists of a divergent reproduction of the elements through the system’s elements, i.e., in an unexpected, surprising communication. This also takes in ‘‘coincidence,’’ which here should not be seen as causal-theoretical, or, as for Hegel, as a counter-term for necessity. For Luhmann, coincidence should be seen as a form of coherence between system and environment that evades the system’s synchronization (control, systematization). No system — or organizational system — can control for all causalities. In Luhmann’s sense coincidences should be seen as a system’s ability to take advantage of ‘‘events’’ that are not produced by the system itself (i.e., not in autopoiesis’s network). They are at once possibilities, chances, and dangers. They can be constructive while also destructive. They can turn out to be ‘‘order from noise,’’ or as structural connections that channel irritations from the environment. Even so, variation falls into place under the system’s autopoiesis; it ensures that the communication continues — if one with freer connective possibilities and an inherent tendency toward conﬂict. But like all operative elements in dynamic systems, communication and deviant communication (innovation) are situationally determined and rapidly lose their meaning. The concept of variation therefore gives no answer to the question of why great epoch-making ideas and inventions arise, for evolution makes no great leaps, even though in retrospect others might observe it as doing so. The social reality is extremely conservative and does not negate the

16 Cf. Luhmann (2000): ‘‘The function systems proceed from inclusion, exclusion is unplanned and, so to speak, just happens. In organizations, the opposite is the case. Here, everyone is excluded — membership is not a natural right — since inclusion must proceed in a highly selective fashion’’ (p. 392, own translation).

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existing in favor of the unknown, if chances for consensus are not proven or even tested for the same situation. A variation can therefore make it through in favorable cases, but it is not possible to pose the question of why things should be so and not otherwise. Selection. This relates to the system’s structures or the communication-controlled expectations that govern through the media. A meaning basis is chosen that is expectation producing and that works to condense, and innovations are rejected that are not considered suitable as guidelines for the continuing communication. When variation and selection are ‘‘coincidentally’’ linked, one can develop a theory of evolutionary selection separately. It arrives here and there at a structural innovation ‘‘whatever its causes,’’ as Parsons says. Every variation has a compulsory selection as a result. The system selects the existing state and not innovations, such that the system can only remember and compare. When it comes to the ‘‘unlikely likelihood’’ one can say that variation occurs constantly, ﬁrst when an ‘‘event’’ is selected until a condensed structure turns up something unlikely, namely a marked deviation from the original state. Re-stabilization. This refers to the state of the evolutionary system after a positive or negatively charged selection. It is a question of durability in a social system differentiation. The innovated structures must at a point in time ﬁt in with the system and be made compatible with their relationships with their environment. In 1789 the Paris uprising was observed as ‘‘revolution’’ and this later had consequences for the development toward a representative democracy. The codiﬁcation of the legal system, the liberation of an economic system with its own forces, the secularization of religion, and the privatization of the extended family can be seen as re-stabilizations of the revolutionary innovations. But revolution can also be harnessed negatively, as in Prussia where one ended up with a re-stabilization of a culture-state program for schools and colleges. Therefore, variations can vanish unobserved, while selections are normally held on to in a system memory. However, the point here is that a rejected variation in the long run can have greater effect than an implemented innovation. In any event, the term re-stabilization represents sequences for building in structural changes in a structurally determined operational system, and this bases itself on an insight that this happens through variations and selections, and always through the system’s own operations. In every case selection (positively or negatively) leads to an increase in the system’s complexity and therefore the system must react with re-stabilizations. Banks are examples of re-stabilizations of the money economy, and burst the old maxim about reciprocity. The ‘‘new-time’’ state earns its re-stabilization from a long-prepared political centralization. Re-stabilization always carries with it a stability principle, but always with an additional solution: that which will abolish atomic power stations are confronted by the question: How will we generate power another way? With the transition from the re-stabilization function to functional systems stability becomes a dynamic principle and indirectly a central stimulus for variation. Functional systems remain prepared for change under the condition of functional equivalence and a net superiority of possible new forms.

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Also, while they do not themselves initiate innovations, they have a high potential for reacting to innovations with innovations. This applies to a greater extent if within functional systems organizations are formed that can change themselves and their decision-making practices through decisions. Luhmann (1997) describes this in the following way: As early as the pronounced stratiﬁed order of the middle ages corporations, such as churches, monasteries, orders, cities, guilds and universities, assumed innovative functions — at ﬁrst because they, thanks to their corporative stability, could maintain themselves as member communities outside the system of estate. Society had already started experimenting with forms of dynamic stability, that were not anticipated in its form of differentiation. But exactly this division of labor between the corporations also implied that their potential for innovation remained restricted to themselves and, in the transition to the modern world, they were perceived as rather rigid and inﬂexible [institutions]. The system of estates and corporations was gradually replaced by the order of organizations in functional systems; only then the primary societal subsystems were given the opportunity to develop a conditioned dynamic stability. (p. 493, own translation).

In the course of this evolutionary process the functional systems realign their mode of selection more and more toward fundamentally unstable criteria. Provided the channeling of the re-stabilization takes place within organizations, i.e., through decisions, resistance toward novelty will also be produced.

6 Conclusion In today’s climate of forced dynamic and social change there runs the classic solution pattern in organizations, namely the reproduction of ‘‘more of the same’’ problem: the same — that is to say the repetition of a pattern — at the same time embraces change of the pattern by allowing for the growth of new emergent structures. Innovations are thus important even though they have their limitations (seen from a system-theoretical perspective). But innovation can only assume the form of being a disturbance of experience and routine, and in this sense innovation is unlikely (Luhmann, 2000, p. 162). Proposals for innovations almost by necessity invoke conﬂicts, and it would be foolish of organizational sociologists not to emphasize the importance of precisely these aspects of the reality. Every innovation or reform is about ‘‘creating or utilizing previously unrecognized social spaces’’ the organizational theorist P. Herbst observed (cf. Herbst, 1976, p. 48). These empty spaces will no doubt be formally unregulated areas in an organization. But we can ask with Luhmann if such empty spaces still exist, and if all reform and innovatory work in organizations to an increasingly greater degree is no more than the entering of already occupied spaces. It is therefore perhaps time to reﬂect upon the question: How will organizations in the future endure with their self-inﬂicted innovations? In a follow-up chapter in this volume, we carry out a deeper exploration of the topic launched in this chapter by setting the theme of autopoietic understanding of

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‘‘innovative organization’’ against more traditional views from the literature on innovation in organizations.

References Ahlemeyer, H. W. (1997). Mehr des Neuen statt mehr desselben?. Vom Umgang mit Innovation in organisierten Sozialsystemen. Sozialwissenschaften und Berufspraxis, SUB, Heft 4, pp. 329–340. Ashby, W. R. (1956). An introduction to cybernetics. London: Chapman and Hall. Baecker, D. (2000). The distinction theoretical shift in systems theory. An interview with Dirk Baecker. Distinktion (1), 123–131. Baecker, D. (2007). Studien zur na¨chsten Gesellschaft. Frankfurt am Main: Suhrkamp. Bakken, T. & T. Hernes. (Eds). (2003). Autopoietic organization theory. Drawing on Niklas Luhmann’s systems theory. Copenhagen: Liber, Copenhagen Business School Press. Bakken, T., & Hernes, T. (2008). Autopoiesis. In: R. Thorpe & R. Holt (Eds), Sage dictionary of qualitative management research (pp. 33–36). London: Sage Publications. Bateson, G. (1972). Steps to an ecology of mind. New York: Ballantine Books. Beckert, J. (1997). Grenzen des Marktes: Die sozialen Grundlagen wirtschaftlicher Efﬁzienz. Frankfurt am Main: Campus Verlag. Berger, P. L. (1988). Robert Musil und die Errettung des Ich. Zeitschrift fu¨r Soziologie, 17(2), 132–142. Brunsson, N. (2002). The organization of hypocrisy: Talk, decisions and actions in organizations. Oslo: Abstrakt Forlag. Giddens, A. (1990). The consequences of modernity. Cambridge: Polity Press. Gu¨nther, G. (1980). Die historische Kategorie des Neuen. In: G. Gu¨nther (Ed.), Beitra¨ge zur Grundlegung einer operationsfa¨higen Dialektik (Vol. 3, pp. 183–211). Hamburg: Felix Meiner Verlag. Herbst, P. (1976). Alternatives to hierarchies. Leiden: Martinus Nijhoff. Joas, H. (1992). Die Kreativita¨t des Handelns. Frankfurt am Main: Suhrkamp. Krohn, W. & G. Kru¨ppers. (Eds). (1992). Emergenz. Die Enstehung von Ordnung, Organisation und Bedeutung. Frankfurt am Main: Suhrkamp. Luhmann, N. (1971). Politische Planung. Wiesbaden: Westdeutscher Verlag. Luhmann, N. (1973). Zweckbegriff und Systemrationalita¨t. Frankfurt am Main: Suhrkamp. Luhmann, N. (1975). Soziologische Aufkla¨rung (Vol. 2). Wiesbaden: Westdeutscher Verlag. Luhmann, N. (1978). Organisation und Entscheidung. Wiesbaden: Westdeutscher Verlag. Luhmann, N. (1995). Social systems. Palo Alto, CA: Stanford University Press. Luhmann, N. (1988). Die Wirtschaft der Gesellschaft. Frankfurt am Main: Suhrkamp. Luhmann, N. (1991). Soziologie des Risikos. Berlin, NY: Walter de Gruyter. Luhmann, N. (1995). Sich im Undurchschaubaren bewegen. Zur Vera¨nderungsdynamik hochentwickelter Gesellschaften. In: R. Grossmann, E. Krainz, & M. Oswald (Eds), Symposium vera¨nderungen in organisationen, Gabler Verlag, Wiesbaden. Luhmann, N. (1996). Entscheidungen in der Informationsgesellschaft. Manuscript, Berlin. Luhmann, N. (1997). Die Gesellschaft der Gesellschaft. Frankfurt am Main: Suhrkamp. Luhmann, N. (2000). Organisation und Entscheidung. Wiesbaden: Westdeutscher Verlag. Maturana, H., Varela, F., & Uribe, R. (1982). Autopoiese: die Organisation lebender Systeme, ihre na¨here bestimmung und ein Modell. In: H. Maturana (Ed.), Erkennen: Die Organisation und Verko¨rperung von Wirklichkeit. Braunschweig/Wiesbaden: Friedr. Vieweg und Sohn.

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Musil, R. (1979). The man without qualities. London: Secker and Warburg. Varela, F. (1979). Principals of biological autonomy. New York, NY: North Holland. Watzlawick, P. (1985). Management oder — Konstruktion von Wirklichkeit. In: G. Probst, H. Siegwart (Eds), Integriertes management. Bausteine des systemorientierten Managements (pp. 365–376). Stuttgart. Weick, K. (1969). The social psychology of organizing. Reading, MA: Addison-Wesley. Weick, K. (1982). Management of organizational change among loosely coupled elements. In: P. S. Goodman, et al. (Eds), Change in organizations: New perspectives in theory, research, and practice. San Francisco, CA: Jossey-Bass. Whitehead, A. N. (1978). Process and reality. An essay in cosmology. New York, NY: Free Press. Wittgenstein, L. (1967). Philosophical investigations. Oxford: Basil Blackwell.

Chapter 5

Autopoiesis and Organizations: A Biological View of Social System Change and Methods for Their Study Chris Goldspink and Robert Kay

1 Introduction Many approaches to understanding organization change approach ‘‘the organization’’ as a relatively static entity. Punctuated equilibrium models have also become popular, but here too the notion of unfreeze–change–refreeze suggests change as an exception — a break with the more normal stability upon which organizational control is predicated (Taplikis, 2005). By contrast, Tsoukas and Chia (2002, p. 570) have argued that ‘‘Change must not be thought of as a property of organization. Rather, organization must be understood as an emergent property of change. Change is ontologically prior to organization — it is the condition of possibility for organization.’’ Intuitively we agree with their position. However, it raises some signiﬁcant questions for practitioners, principal among them: If change is constitutive of the organization rather than something which managers can control, then to what extent can change be subject to strategic inﬂuence? The problem implied by this question can be resolved to some extent by appreciating that it is change at one level which inﬂuences stability at another. We typically refer to this phenomenon using the concept of ‘‘emergence.’’ The concept has however been criticized as a cover all — used to appear to explain what we cannot currently explain in scientiﬁc terms (Clayton & Davies, 2006). It is here then that the micro–macro problem takes hold. Emergence remains particularly controversial when applied to social science (Sawyer, 2001, 2005). The reason is that the mechanisms of emergence within social systems can be expected to be different from those present in other natural systems, due to the presence of cognitive agents (Castelfranchi, 1998;

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Ellis, 2006; Goldspink & Kay, 2007, 2008). How they are distinct is made clear from the application of autopoietic theory. We use autopoiesis to better understand the reciprocal interplay between the micro behavior of agents on the one hand, and the resulting pattern of behaviors at the macro-level on the other. These emergent macro-structures are somewhat robust patterns associated with particular groups of agents. They have traditionally been referred to using terms like ‘‘institutions,’’ ‘‘norms,’’ and, the focus of our interest here, ‘‘organizations.’’ These patterns do not result from upward causation only, as is the case with particle interaction, for example, but rather they include a downward causal path: constraining the scope of action of the very agents which give rise to them. In other words, macro-level patterns have micro level effects. This has been referred to as ‘‘immergence’’ (Castelfranchi, 1998). Fuchs and Hofkirchner (2005, p. 33) take an emergentist perspective in his classiﬁcation of alternative approaches to the micro–macro relationship within social theory distinguishing between individualist approaches such as agency theory, ‘‘sociologism’’ including systems theories which he argues are top-down deterministic, dualistic approaches and dialectical approaches. Most social theory falls into one or other of the ﬁrst two categories. These theories work with a dichotomous view of macro and micro: focusing attention on just one level or the other and failing to address their relationship. This is consistent with Weik’s (2006) view. She has argued that social theory can be divided into three categories: dualist, duality, and theories which avoid or deny the separability of micro and macro. In social science the micro–macro problem is also referred to as the problem of structure and agency. Structure emerges from the agency of social agents and at the same time constrains it, but neither determines the other. Weik argues that in most social theory this micro level capacity for partial independence is commonly attributed to intention or purpose — the debate being about how ‘‘free’’ agents are to exercise these with respect to structure. Structure implies a repetitive relation between two or more individuals with different theorists positing different dimensions to that relation — viz. shared knowledge, functions, routines, constraints reciprocal expectations, power or force, rational choice, identity need, habit or rule following. ‘‘Some of these deﬁnitions overlap, some have been taken together to form several levels of structure embedded in one another y and some are, of course, contradictory’’ (Weik, 2006, p. 3). Another area of confusion relates to where these structures are considered to reside. ‘‘The most prominent candidate is, of course the individual mind’’; however, alternatives include the brain, body, human essence, act (habitus), and language. Finally, the mode of inﬂuence between levels is often unspeciﬁed: is it causal or something else? In short, social theory has attempted to resolve the problem using a wide range of conﬂicting theoretical stances, none of which have proven satisfactory. Those that come closest are in Fuchs dialectical category. These too are diverse, including but not restricted to Marxist dialectical materialism, the critical theory of Habermas, the critical realism of Bhaskar, and the structuration theory of Gidden. A few incorporate the theory of autopoiesis with some drawing directly on Maturana and Varela’s original work, whilst others have adopted a more Luhmannian perspective. What then does it offer?

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2 Autopoiesis Before illustrating what an autopoietic view can bring to addressing micro–macro interplay to understand real organizational dynamics, it is worth providing a brief overview of the key elements of the theory and its implications. The theory was developed to provide explanations of the nature and characteristics of living systems (biological cells and metacellular organisms). The central idea is that living systems are characterized by their self-production: the components of the system producing the components of the system. A key implication of this is that the requirements for the maintenance of self-production constrain the way in which individuals can interact with and ‘‘know’’ their environments. Within autopoietic theory, an individual’s behavior is determined by particular states of nervous system activity (Maturana & Varela, 1980); this activity is deﬁned by the concept of operational closure, which presupposes that in all cases nervous system activity results from and leads to further nervous system activity in a closed cycle (Maturana & Varela, 1980). Possible and actual changes in state of the nervous system are therefore deﬁned by the nervous system’s structure and not external forces. External or environmental forces may act as triggers for change, but it is the nervous system’s structure that dictates which forces can be a trigger (Mingers, 1991). Therefore, changes to the structure of one person’s nervous system, and consequently their behavior, will be unique to that person. The environmental perturbations that act as a change trigger in one person will not necessarily trigger a change in another, or if they do, the change that is triggered may take a different form and/or have different implications for the viability of that person in his/her environment, given his/her history. Individuals may contribute to the emergence of a stable pattern, but they do so by acting on the basis of their unique history. Although the nervous system is operationally closed it is plastic, its structure changes over time, and it is this quality that allows for changes in behavior and subsequently what we describe as learning (Mingers, 1991). Therefore, as the state of the nervous system changes, so too will the potential range of behaviors that its structural determinacy makes possible. The term used for this history of structural change is ontogeny (Maturana & Varela, 1992). Barandiaran (2005) has argued that the advent of the central nervous system in organisms allows them to exploit the rapid response times of the neural system supporting a signiﬁcantly increased set of responses to environmental perturbation. The responsiveness of the central nervous system may be further enhanced to the extent that it operates as a far-from-equilibrium system, at the edge of chaos, as has been argued within the emerging ﬁeld of neuro-dynamics (Kelso, 1995; Rocha, 1996; van Gelder, 1998; Thompson & Varela, 2001; Cosmelli et al., 2007). It is the resulting asymmetry between the state space of possible conﬁgurations and the range of response needed to maintain immediate regulation in a given environment that gives rise to the ‘‘agency’’ that is of concern to social emergentists. According to Barandiaran, ‘‘The higher the agent’s capacity for adaptively guided self-restructuring (plasticity) the higher its behavioral adaptive autonomy and hence its agency’’ (2005). Autopoietic theory therefore casts a light on the nature and origins of agency

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fundamental to understanding social emergence, specifying the biological processes that support and constrain it. Hejl preempted this more recent perspective, referring to it as ‘‘cerebral overcapacity’’ (1993a, 1993b). He noted that it conveyed both advantages and disadvantages to the agent. The advantage is that a capacity to generate a wide range of responses (requisite variety) improves the agent’s survivability in a wide range of environments. The disadvantage is that this plasticity contributes to the contingent nature of agent–agent and agent–environment interactions. It dramatically increases the nonlinearity of the system and hence reduces its stability: it is a double-edged sword. The resulting variability can therefore only be harnessed by the agent to the extent that it can be channeled or constrained at least over short time frames. Hejl notes, ‘‘The only ‘solution’ to this problem seems to be society’’ (Hejl, 1993a, p. 229). In other words, social structures represent dynamic attractors, which imply a temporary reduction in complexity. This supports agent viability in the short term while at the same time giving up none of their intrinsic and open-ended ﬂexibility to adjust to changing circumstances in the medium term. These social attractors which provide a temporary reduction in complexity are a product of the recurrent interaction — structural coupling in Maturana and Varela’s terminology — between agents, in the context of a feedback path between structure and agent. We have argued (Goldspink & Kay, 2003, 2004) that structural coupling is the mechanism by which all social structures emerge and are maintained, including those we refer to as organizations. Thus, structural coupling constitutes the generative mechanism which gives rise to social organization. Structural coupling implies the coordination of behavior between agents — the behavior of one agent triggers a reciprocal behavior in those with which it is coupled as part of a closed network or domain of reciprocal interaction. Maturana and Varela refer to a domain of coupling as a phenomenal domain. In other words, when considering social systems, we are looking at self-organizing phenomena. It commonly results in the formation of nested hierarchies and heterarchies of phenomenal domains. When organized hierarchically, each domain constrains the range and scope of behavior of that above it. Intersecting domains (i.e., domains which include some common agents) within a heterarchy perturb one another and may themselves become structurally coupled. In human social systems the hierarchy will include behavioral and linguistic domains, and the heterarchy will comprise the many social domains any individual may participate in simultaneously (family, club, work group).

3 Bridging the Micro–Macro Divide Our approach brings together the autonomous agent ontology of autopoietic systems described above with complexity theory. Autopoiesis provides a model of how macro (social) phenomena emerge from the complex (and nonlinear) interplay between the heterogeneous agents (people) which make up a social system. Complexity theory

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allows us to explain the resulting dynamics by describing the generative processes that result when these agents enter into recurrent interaction and become structurally coupled. From this perspective, social systems can be seen as a speciﬁc class of complex system, and it is autopoiesis which clariﬁes the distinguishing characteristics of the constitutive agents and hence the range and class of behavior which can emerge. In particular, it provides an account of the cognitive range and resulting linguistic/reﬂexive character of social agents (Goldspink & Kay, 2007). This, of course, represents an emergentist view, but one very different from that involving physical systems (Davies, 2006). In human social systems, including organizations, there is an additional feedback loop made possible by the fact that human agents can observe at a distance, distinguish pattern at the social level, recognize themselves as contributors to that pattern, and change their behavior accordingly (Goldspink & Kay, 2008). From this perspective an organization’s apparent coherence is a product of self-referential cycles (Hejl, 1981, 1984, 1993) generated as emergent structure results from agent interaction and then feeds back to constrain agent behavior. In order to understand the change that is producing the patterns characterizing an organization at a given time, there is a need to pay attention to this dialectic between macro- and micro behaviors. In human social systems most of the action happens in and through the linguistic coordination of the coordination of action (Maturana & Varela, 1980; Maturana, 1988a, 1988b). This is to say that human social structures arise and are maintained in linguistic phenomenal domains. The way in which people place themselves in the context of organizing, as well as the way in which they make sense about others, and place themselves in relation to physical (e.g., building layout, geography) and social artifacts (such as norms, rules, and structures, and information technology) will largely be revealed in the way they use language. Language use will therefore reveal a great deal about the constitutive mechanism of the organization as a distinct social phenomena. The emerging linguistic nexus will contain nested patterns of stability and points of potential instability, which provide targets for study and intervention. It is our proposition that for an organizational change intervention to be effective it needs to be designed with an appreciation of the patterns and drivers that, in the words of Tsoukas and Chia, describe the change dynamics from which the organization emerges. These will be speciﬁc to an organization at a given point of time. In complex systems terms, the patterns are attractors of the system and the drivers are the states of variables that maintain the operation of the system on any particular attractor. But what are the variables? The state space of a social system comprises a dimension (degree of freedom) for all of the behaviors that can be generated by the agents constituting it. In human systems such as organizations, this includes linguistic behavior. Language is highly ﬂexible and recursive (distinctions on distinctions), and as a result the state space it supports effectively has inﬁnite dimensionality. It is this vast space of possibility which, as we have already discussed, is the basis for agency and which explains the inherent ﬂexibility of social systems. However, as with many complex systems, at any particular time a much more limited set of behaviors may explain the dynamics at the level of interest. It is this limited

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subset of behaviors which we refer to as the drivers of the change dynamics of the organization. These are the relatively small number of behaviors (including linguistic utterances) which generate and maintain a particular attractor. These attractors are of course the cultural norms and institutions which combine to support the higher order attractor, which we refer to as ‘‘the organization.’’ For many managers, detecting these drivers is an intuitive process or one based on experience; however, more systematic research methods may also be used to surface them. Once the key drivers inﬂuencing such patterns have been identiﬁed, the manager can take action to disrupt those that appear to support undesired stability and/or stimulate those that might support desired change. While developments in complex systems and social simulation have advanced our ability to map complex dynamics, this has generally been in systems where agents have limited cognitive capacity (Sawyer, 2003, 2005). While developments in these techniques hold promise for the future, at the current time there are few techniques that support our understanding of dynamics that result from the reﬂexive emergence associated with human agents (Goldspink & Kay, 2007, 2008). It is therefore necessary to use more conventional research methods to gain insights into the operations of organizations. A range of methods have been developed for the study of linguistic interaction. Some focus on mapping the denotative content of utterances, while others are concerned with the illocutionary or pragmatic force of language as a basis for direct inﬂuence (Searle, 1969; Habermas, 1976).1 In the following two cases we illustrate techniques that can be employed to surface the change dynamics from which the organization emerges, focusing primarily on alternative methods for linguistic analysis.

4 The Case Studies Normal qualitative or quantitative techniques will often provide a static snapshot of pattern at one or more levels, but leave much of the generative process unclear. In particular, many conventional methods, founded as they are on functionalist reductionism, fail to support any analysis of the interplay between micro and macro levels. However, creative recombination of existing techniques sometimes makes them more useful. In the ﬁrst case study we combine two well-established methods, narrative analysis (Bruner, 1991a, 1991b; Snowden, 2001; Browning & Boudes, 2005) and repertory grid technique (Fransella et al., 2004; Jankowics, 2004), and illustrate how these can be used in combination to generate deep insights into factors which inﬂuence the dynamics of an organization. In the second case we use analysis of the illocutionary force of language to identify inﬂuence patterns associated with governance of an institution.

1

We are examining this in another case study on normative self-organization in the Wikipedia; see Goldspink (2007).

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Case Study One: Financial Services Trust and Innovation Potential

The research context was a small business unit within a large ﬁnancial services institution. The business unit in which the case study was conducted was led by a general manager and a six heads of departments, each with multiple direct reports in a strongly hierarchical structure. Each department in the business unit was responsible for the management of different outsourcing arrangements and contracts with suppliers. The leadership team was concerned at the low level of collaboration between the different departments and the effect this had on innovation and the quality of decisions making. In response they designed a small intervention to facilitate collaboration across the business unit. The task involved bringing together senior managers from the different departments to solve a set problem. The managers were asked to establish a taxonomy against which the top 100 suppliers could be categorized, according to whether they were strategic (bringing new capability), aligned (providing improved capability to an existing strategy), or standard (providing supply to a nonstrategic function). It was intended that the taxonomy would form the basis for new relationship management models. Participation in the project was voluntary and undirected: those who volunteered to participate were expected to self-organize in order to clarify and generate strategies to address the problem. The voluntary nature of participation resulted in only about half of the potential participants taking part. The outcome of the project was seen by most people associated with it, including the general manager, to be unsatisfactory, both in terms of the proposed solution and the collaboration achieved. The group working on the project fragmented into two subgroups with each advocating incompatible solutions. The fact that such a relatively simple task could not be completed came as a shock to the general manager, who suspected there were deeper issues at play. We were asked, as people independent of the institution, to explore the reasons why the exercise failed. Our brief was to understand the factors affecting the group’s ability to collaborate: why couldn’t a group of intelligent, experienced managers, organize themselves to complete a relatively simply problem-solving activity?

4.1.1 Methodology. Eleven senior managers took part in our study drawn from a group of 18 possible participants. Participants were selected at random from a list of all the senior managers. Six out of the eleven interviewees had taken part in the exercise, whilst the others, although aware of it, had either speciﬁcally chosen not to be involved, or had sent a representative from their team. We sought to gain an understanding of the recent history of the interactions, the environment, and how both individual (micro) sense-making and (macro) institutional structures combined to limit collaboration. To achieve this, a methodology which combined narrative and repertory grid methods was employed. Both narratives and the repertory grids were collected in a single interview which lasted on average about one and half hours.

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Narrative. Narrative is seen from a number of perspectives within the social and organizational sciences. Most commonly it is encountered as a method — one particularly appropriate to: y examine the interconnectedness of human agency and social structure and the temporality of historical events in processual ways. (Gotham & Staples, 1996, p. 481)

It has, however, been argued to be at the core of the functioning of human meaning making — the narrative mode of thought (Bruner, 1991a; Dautenhahn, 2002). Bruner observes that there is a sense in which: y narrative, rather than referring to ‘reality’ may in fact create or constitute it y . (1991, p. 13)

From this perspective, narrative data provides an account both of how people interpret past events and how those interpretations play a role in embedding particular ways of thinking and knowing in the culture of the organization — how they come to be constitutive of the organizational reality. When we construct narratives we place ourselves as a character, even if it is one of innocent bystander. A narrative can reveal a lot about the part and future role an actor may play. We can and do of course revise our narratives. We will, however, be very reluctant to change the central character — ourselves: the grand narrative that is our sense of identity. Narrative data then provides insight into the relationship between events — i.e., how the observer/participant sees the ways events are linked in time. More than this, and signiﬁcantly for this study, it captures individual and collective accounts of the interplay between individual behavior and collective consequences. These accounts play a part in the maintenance of existing order and/or to reﬂect the basis for change in established routines by revealing compartmentalization in the linguistic domains. In this case study a very simple narrative collection was undertaken. This involved asking participants to recall two recent collaboration experiences with which they had been involved within the institution: one a positive experience and the other a negative experience. Not all participants were able to think of two stories that they felt were worth telling, and as a result 14 stories were collected out of a possible 22. The stories were analyzed with the participant at the time of the interview. Six key events were selected that ‘‘stuck in their mind.’’ These events were equivalent to what David Snowden (2000) would describe as an anecdote. Breaking the stories down into anecdotes supported analysis of the stories as a whole, but also identiﬁed discrete events for subsequent thematic analysis across narratives. Eighty-four separate anecdotes were collected and clustered according to commonalities in their content, i.e., common words, depiction of similar events, etc. Grid interviews. Personal Construct Theory was developed by George Kelly (1963) in the 1950s. Central to the theory is the idea of constructive alternativism (Bannister & Fransella, 1989). This simply states that any event or situation is subject to alternative construal by different individuals. An event can carry many different meanings, and the meaning it carries for any individual will depend on how he/she construes it at that time and how it ﬁts (its implication) within his/her existing construct system. His/her existing construct system is a product of prior acts of

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construal and forms a hierarchical system of more or less tightly held conceptual distinctions which orientate behavior. Kelly saw this construct system as dynamic — being constantly modiﬁed as the agent acts in the world and attempts to be effective within it. While a construct system is speciﬁc to the individual and forms the basis of that individual’s agency, it is a product of his/her history of interaction in the current and other social domains. Constructs low in the hierarchy have fewer dependent connections with other constructs and can be surrendered or modiﬁed more readily than those at the top of the hierarchy. Superordinate constructs form primary orientating distinctions: they are associated with worldviews, and individuals will generally be reluctant to change them as they have profound implications for the way he/she sees and orientates him/herself in the world. Kelly (1963) argues that all social processes necessarily involve the mutual construal of others construction and that this gives rise to some commonality of construction (consensuality) in that domain of interaction. Repertory grid (Fransella et al., 2004; Jankowics, 2004) is one of a family of related methods developed by Kelly and others to make Personal Construct Theory operational. In the context of this case study, repertory grid offered a means for mapping both individual (micro) and collective (macro) patterns of construal within a particular social domain. Furthermore, grid analysis supports the development of metrics, which allow some prediction of how willing or likely individuals would be to change their construal and thus how responsive they may be to alternative change interventions. Repertory grids collect ﬁne-grained data about individuals sense-making about some target. While the data is ﬁne grained it is also sharply focused, so the challenge in using grid as a means for data collection is to ensure that the data converges well onto the topic of inquiry. Critical here are the choice of items of experience (elements) that will be used to ‘‘elicit’’ ‘‘constructs’’ and the focus question used during elicitation (Jankowics, 2004). Elements need to be tangible items of experience (i.e., time-bound events, things, or people). For this exercise we chose to use relational descriptors as prompts and to have the respondents supply speciﬁc people who matched the descriptor.2 These people then became the elements in that respondent’s grid. Each respondent would have different individuals, but individuals who were selected against criteria were common to all respondents. Respondents were asked to identify eight colleagues from within the senior manager team who matched the following descriptions:

2

A colleague with whom I share information A colleague with whom I don’t or seldom share information A person who is senior to me from whom I learnt a lot

In a more recent related study, which focused on innovation rather than collaboration and trust as with the case study reported here, in this latter case ‘‘innovation events’’ were taken from the narratives and used as elements.

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A A A A A A A

Chris Goldspink and Robert Kay person who is senior to me from whom I learnt a little ‘‘direct report’’ with whom I share information ‘‘direct report’’ with whom I don’t share information colleague whom I trust implicitly colleague whom I don’t trust colleague I feel comfortable asking for advice colleague I don’t feel comfortable asking for advice

These descriptions were considered to capture qualities of relationships associated with collaboration and also to assemble into an approximate continuum of relational strength. The minimum quality of relationship upon which any level of collaboration could be built was taken as a ‘‘willingness to share information.’’ Above that would be a relationship in which the respondent would be ‘‘comfortable asking advice’’; ‘‘learn from’’; and ‘‘trust implicitly.’’ Constructs were then elicited using the triadic method (Fransella, 1977) using the comparison question ‘‘Which two of these people is similar to one another and different from the third in terms of how they helped or hindered collaboration?’’ The answers were captured directly on a grid and scored by the respondent in the normal way. 4.1.2 Analysis. All the people involved selected and described the same negative experience — the exercise in generating collaboration discussed earlier. As might be expected the narratives captured quite distinct and different accounts and interpretations of events: unique personal histories of the shared experience. These narratives provided anchoring events against which the individual sense-making of the participants (as revealed by the repertory grids) could be interpreted. They also revealed the wider environmental factors and historical sequence, as well as the individuals reading of cultural rules, norms, and institutional practices, which they believed inﬂuenced the outcome. Grids were analyzed using the software package Idiogrid (Grice, 2002). Patterns in the relationship between elements and constructs were examined using Principal Component Analysis. This enabled us to identify, for each respondent, the type of person he/she was likely to share information with compared to those with whom he/she would be unlikely to share; what type of person he/she would trust compared to those he/she would not trust etc. It also revealed the degree of association between the element classes; if likelihood to ‘‘share information’’ was closely associated with ‘‘trust’’ or based on different factors in a relationship for example. According to Kelly, a person’s construct system provides him/her with a basis for hypothesizing about consequences of his/her and others’ actions. Tight construal (as indicated by a high mean correlation between constructs in the grid) would suggest that a respondent would have relatively unvarying predictions based on his/her construal of a situation. In other words, the characteristics the respondent attributes to individuals would, from his/her perspective, be expected to provide good prediction of the collaborative behavior of others. Loose construal, by contrast,

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would suggest a person with more ﬂexible views, someone open to surprise. Inferences can therefore be drawn about a respondent’s openness to change. In addition, an ordination score can be used to reveal the location of a construct within the respondent’s construct hierarchy, with higher scores suggesting higher ordination or more meaningful (and abstract) constructs (Landﬁeld & Cannell, 1988). Individuals are less likely to be willing to change higher order constructs as they have signiﬁcant implications for how they make sense of the world (Kelly, 1963; Bannister & Fransella, 1989). Combining the results. A comparative analysis of the results of the two data sets was undertaken on two levels. Firstly, individual stories were mapped to individual repertory grids. These two data sets revealed insight into which constructs in each individuals meaning system primarily orientate their construal of events and guide their action. Secondly, the narrative clusters emerging from the thematic analysis of the stories were mapped to the output from the group grid analysis. Usually repertory grid analysis is undertaken at the individual level; however, in this instance we conducted a thematic analysis across the constructs of the entire group (see Jankowics, 2004 for a systematic process for doing this). This analysis provided insight into how each agent made sense of their situation and the degree to which there were commonalities to this sense-making. Mapping these two together revealed the areas of common construal around a distinct series of events. It also means we could see the depth with which that construal is held and therefore also which dimensions of the social system’s patterns can easily change, and those that will not. Observations. From the combined analysis it was possible to discern the primary distinctions that orientated respondents toward one another and inﬂuenced their willingness to collaborate. These distinctions appeared to form the basis for the creation of subgroups within the broader team, where people of like characteristics have a much higher propensity to trust and collaborate with each other rather than those they perceived as being different. The combination of depth with which these constructs were held and the degree to which they were shared across the group strongly drove the eventual outcome of the particular activity we studied, i.e., the group that was supposed to be collaborating split to create subgroups closely aligned to the constructs described above. What is interesting here is that overtly all the participants wanted to collaborate, and indeed initially did collaborate around the problem they had been set, thus creating a new pattern of interaction that had not existed before. However, over a relatively short period, this new pattern broke down with a slightly modiﬁed version of the preexisting pattern of interaction reemerging. In the evidence collected there is a clear explanation for this. Individuals were construed through established constructs, and these inﬂuenced subsequent behavior. As there was nothing in the design of the intervention which was directed at challenging or disrupting the existing ways of making sense of the situation and, in particular, nothing powerful enough to

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compel the need to reconsider deeply held constructs, no change was achieved. On the contrary, the existing patterns reappeared in a slightly modiﬁed form. 4.1.3 Conclusion on Case Study One. This case selected for this research centered on an intervention designed to address a limited capacity for innovation in a senior management team — i.e., a perceived inability for managers to bring new ideas, understandings, and capabilities to challenging situations. We have examined the reasons for the failure of this intervention by seeking to better understand the way in which individuals contribute to maintaining current patterns in the organization and how the intervention failed to address these. This represented a move away from approaches which treat ‘‘organizations’’ in a reiﬁed way to a complex systems view focusing in particular on understanding the interplay between macro and micro levels. The intervention initially used to try to build collaboration in this work unit, assumed that collaboration was not occurring due to formal structural inhibitors (institutional silos and or physical distance) and/or lack of opportunity. It was anticipated that providing different people from different backgrounds with the opportunity to work on a common project would be all that was required to overcome the problem of lack of collaboration. This proved too simplistic as it failed to identify the way in which individual and collective sense-making around who and when to share information or trust had developed within the organization and had come to constrain the range and type of relationships members were prepared to participate in. The data gathered using both narrative and repertory grid methods revealed a more complex picture. The senior management group was shown to have formed a set of ways of interpreting their environment which limited their willingness to engage on the basis of three dimensions of relationship. These were not related to the formal structure or to physical proximity directly (although these would have inﬂuenced the formation and maintenance of the dimensions found) but were culturally stable dimensions which had become self-maintaining attractors. This combined with a pattern of tight construal contributed to a very stable system whereby individuals sense-making reinforced cultural patterns which shaped interaction so as to reinforce individuals sense-making in a manner which restricted the possibility of change. This analysis supported the argument that organizational behavior is a complex product of the interplay between individual agency and institutional structure and that these come together to form phenomenal domains. We have argued that unless insights can be gained into the drivers which support attractors in these domains intervention is likely to be ineffective. We have shown how conventional methods, in this case narrative and repertory grid technique, may be combined to help locate these drivers in the linguistic domain pertaining to the particular context.

4.2

Case Study Two: Wikipedia

This case concerns a less conventional form of institution — an online ‘‘community’’ albeit one which has self-organized to produce a product more commonly produced

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by a command organization. The interest here was to understand how widely dispersed and heterogeneous (in terms of having different skills, knowledge, goals, and resources) agents can come together to produce a credible encyclopedia. This case is being undertaken as a part of the EU funded research project titled Emergence in the Loop (EMIL). EMIL is explicitly concerned with the micro– macro problem and is using both empirical and computer simulation methods to advance our understanding of it. The insight behind the EMIL project is that this two-way interpenetration of micro and macro levels is fundamental to ‘‘normative action’’ in social systems. Agents perceive higher order social structures (norms) and (perhaps) change their (micro) behavior in response, thus at the same time acting on the norm (perhaps reinforcing it or diminishing it). The case studies chosen therefore involve the study of the emergence of social norms. First among these is that of Wikipedia. Wikipedia is of interest as the individuals that that have participated in creating it appear, through their collective action, to have emerged a set of permissions, obligations, rules, and norms which bring it into being and maintain it as a social system: it has bootstrapped itself into being. Signiﬁcantly, this was not intended or foreseen by those who initiated it (Sanger, 2005). From a governance perspective there are very few means within Wikipedia by which formal control can be exercised, and it therefore relies on emergent self-regulation to function despite signiﬁcant perturbation from ‘‘vandals’’ (task saboteurs), ‘‘trolls’’ (social saboteurs), and turnover of contributors in the context of a task which may require the resolution of emotionally and value-based conﬂict. That said, the theoretical lens of norms adopted by the EMIL project is problematic. Normative theory has functionalist origins and reﬂects the confusion surrounding the macro–micro problem. There remains considerable confusion, for example, as to whether ‘‘norms’’ are best ascribed as ‘‘in’’ the environment or ‘‘in’’ the agent. There is debate also as to whether normative order is the result of agents applying rules or reﬂects pattern which appears ‘‘as though’’ it is rule based. We approach it here through our own theoretical lens as outlined earlier in the paper. Wikipedia can help us to understand:

How people inﬂuence one another and converge on common expected patterns of behavior The emergence and role of social constructs which have become somewhat ‘‘reiﬁed’’ within a particular consensual domain (rules and explicit norms) in an open volunteer community where there is little to no hierarchy and limited capacity for formal sanction and which must continue to attract and retain agents if it is to survive (is in a sense self-maintaining and producing) How these norms and rules are generated and maintained within behavioral and linguistic domains The relationship between goal, technical artifacts, and social structures and the exercise of individual agency within the resulting domains

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Chris Goldspink and Robert Kay Methodology.

In Wikipedia there are two classes of activity:

editing activity; and conversation about editing activity.

As this study was not concerned with the editing activity but with the selforganizing and self-regulating phenomena which make it possible, the Discussion pages of a sample of controversial and featured articles were analyzed. Controversial articles were chosen as they were more likely to involve the need to resolve conﬂict and hence place greater demand on effective normative regulation; featured articles by contrast may be so rated due to the attainment of a higher level of consensus among participants. The activity on the Discussion pages comprises a series of ‘‘utterances’’ or speech acts between contributors about editing activity and the quality of product. The only means for editors to inﬂuence one another’s behavior (to structurally couple) is through these utterances. On the face of it then, these pages should provide a fertile source to support analysis of how self-organization was occurring and to identify the agent characteristics and mechanisms involved. It was anticipated that the process may involve quite subtle use of linguistic cues. Accordingly sampled pages were coded to a high level of resolution using the Verbal Response Mode (VRM) taxonomy (Stiles, 1992). VRM is very attractive where there is a need (as in this case) to capture many of the subtleties of natural language use that derive from and rely on the intrinsic ﬂexibility and ambiguity of natural language, yet map them to a more formal or axiomatic system needed for computer simulation. A range of additional codes were applied, including: whether a listener accepted or ‘‘validated’’ an utterance; the explicit invocation of norms or rules; the associated deontic command; and the style and focus (subject) of the utterance. For the study we randomly selected a sample of Discussion pages associated with both controversial and featured articles. At the time of the study (May/June 2007) there were 583 articles identiﬁed by the Wikipedia community as controversial and approximately 1900 as featured. The analysis reported here is based on a sample of 19 controversial and 11 featured articles. The most recent three pages of discussion were selected for analysis from each Discussion page associated with the article included in the sample. These were subjected to detailed coding using the Open Source qualitative analysis software WeftQDA. Both qualitative and quantitative analysis was performed. The latter was undertaken by reprocessing the coded utterances such that each utterance constituted a case and each applied code a variable associated with that case. This data set was then analyzed using SPSS and MLWin. 4.2.2 Analysis. How might we usefully think about the Wikipedia as an organization? The volunteers who participate in Wikipedia simultaneously participate in a number of other social domains. For the sake of simplicity we depict just one. Figure 1 shows the situation diagrammatically. Each domain (A–C) is comprised of a

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Domain C

Domain A

Wikipedia Domain

Domain B

Figure 1: Autopoietic agents, structural coupling, and nodes of intersection among domains in Wikipedia. number of autopoietic agents in structural coupling. The Wikipedia domain represents a fourth domain. The agents which comprise it represent nodes of intersection between the other domains. To remain viable in all domains, agents at these nodes will need to satisﬁce the requirements for ongoing viability in the other domain to which they belong. In the case of Wikipedia, the fourth domain is happening virtually — agents interact by observing each other’s editing behavior and by interacting linguistically (asynchronously and by written exchange). Our analysis was designed to identify pattern within this domain. We found a distinctive emergent pattern in the utterances. They typically involved an exchange of assertions delivered with a neutral — i.e., nonemotive style. There are very few explicit praises, or put downs, and few niceties like explicit acknowledgments of one another. Seldom do contributors refer to one another by nickname — the exchanges are rather impersonal. This does not tally with what one would expect if the Wikipedia etiquette (http://en.wikipedia.org/wiki/Wikipedia:Etiquette) had been institutionalized. The featured articles conform a little more closely, but if we assume that the etiquette captures the community’s explicit ideal and the form of conduct which it collectively endorses and strives to achieve (the collective goal), then the actual behavior is signiﬁcantly different from the intended. 4.2.3 What kind of phenomenal domain emerges within Wikipedia? To think about what is happening in the domain of Wikipedia, we can usefully draw on Habermas theory of pragmatics. For Habermas, a successful speech act would be one in which the listener both comprehends and accepts the validity claims made by the sender and thus enters into the intended relationship. The tests of validity include

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comprehensibility, truth, sincerity, and rightness. Thus for Habermas, a speech act only serves to support the maintenance of effective communicative exchange to the extent that it is held as valid by listeners. At the level of the individual agent, what is held to be valid will largely be a product of its past participation in one or more phenomenal domains with the norms or rules typical of that domain. Habermas distinguishes between communicative acts and strategic action. The former is action based on consensus while the latter implies action resulting from the exercise of power or compulsion. The latter is not possible in Wikipedia as there are very few means for compulsion or exercise of formal or authoritative power. The intrinsic openness of Wikipedia means that the majority of exchanges can be expected to conform to the qualities of communicative acts — i.e., bounded and inﬂuenced by normative behavior rather than through the exercise of formal authority, power, or coercion. The existence of community is central to establishing such an environment as the heterogeneity of social backgrounds and experiences of participants coming together incidentally around the task would likely fail to have sufﬁcient power to provide coherence to the relationships unless it had the opportunity to converge locally around an accepted set of behavioral regulators. Do we see any evidence of this type of regulator? The absence of any expression of acknowledgment of emotions and/or similarity of attitude (homophilly) among many contributors suggests that Wikipedia lacks many of the qualities of verbal exchange that would identify it as strong community. Possibly it therefore fails to constitute a distinct consensual domain. It is more consistent with being a place to share coordination of a task. This could suggest that the goal is the primary orientating point. However, the lack of quality of discourse needed to achieve consensus is more indicative of a brief encounter between different and established milieus which struggle to ﬁnd common understanding rather than of a community committed to a common goal (Becker & Mark 1997). This might suggest that the primary inﬂuence of the utterance strategies employed by agents is the consensual domain/s to which they belong in their wider life — not the immediate environment of the Wikipedia. If this were the case then we would expect to see speech acts which are a minimal accommodation: are minimally concerned with establishing understanding and aimed at a pragmatic accommodation or satisﬁcing of presenting demands from different editors. Certainly this is one way of interpreting the patterns observed in the data. Similarly we would expect to ﬁnd that local norms and rules had little effect and that social behavior was primarily inﬂuenced by the socialized ‘‘norms’’ consistent with the editors’ primary domains — that is to say — brought in from outside the Wikipedia. 4.2.4 Conclusions on Case Study Two. In this case we are particularly confronted with the epistemic implications of the theory base we are following. Where do consensual domains begin and end? Does the communicative activity in Wikipedia give rise to a distinct phenomenal domain or can it only be understood by appreciating the domains with which its participants are involved outside of the Wikipedia? As Hejl long ago noted, the attributions of closure to social domains (as compared to physical ones at the level of biological entities) is an epistemic act not

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an ontological one, and it reinforces the view that social systems are not autopoietic in and of themselves. Hejl (1984) distinguished between self-maintaining systems and self-referential systems. He argued that functionally autonomous entities (such as organizations) are abstract; they are self-referential but as they do not ‘‘self-produce’’ in a physical domain, they should therefore be considered as self-maintaining but not autopoietic. Thus both Varela and Hejl identify social systems as belonging to the broader class of autonomous, operationally closed, and self-organizing/self-referential systems but not as autopoietic. Further, the concept of autopoiesis only offers new insight into systems that do self-produce in a physical domain: biological systems as per the genesis of the concept. In relation to other classes of system the concept of operational closure and self-organization are sufﬁcient and equivalent. To revisit some fundamentals, the criteria Maturana and Varela (1980) used to distinguish autopoietic systems are: 1. their principal output is themselves, i.e., they are ﬁrst and foremost self-producing; 2. they bring forth their own boundary as a result of their ongoing process of selfproduction; 3. they are operationally closed and are therefore autonomous — their response to perturbation being entirely determined by their structure; 4. in the case of composite unities there is mutual dependence between the levels of autopoiesis — the continued autopoiesis of the components of a composite unity is dependent on the maintenance of the autopoiesis of the composite unity and vice versa. Criterion 2 refers to the necessary existence of a ‘‘boundary.’’ This is inextricably linked to self-production as it is the boundary, amongst other things, which is to be self-produced. The key issue concerns the required tangibility or materiality of such a boundary. The existence of a physical boundary was an important attribute of autopoietic systems identiﬁed in the earlier work of Maturana and Varela although there was ambiguity about whether the physicality was a necessary condition for a system to be classed as autopoietic. Gaines (1981) identiﬁes, for example, that in Maturana and Varela’s 1975 work, Autopoiesis and Cognition: The organization of the living, from which the above criteria were drawn, the authors permit that an autopoietic unity may be distinguished from its environment by a ‘‘concrete or conceptual operation of distinction.’’ This implies that an autopoietic unity may arise both as an ontological fact and/or as a result of an epistemological act of an observer. If the requirement for tangibility is to be so relaxed, it is still necessary to identify the boundedness of autopoietic systems and to identify how this boundedness is selfmaintained. Mingers (1995) notes that the boundary of a social system is not physical in the way that a cell boundary is physical. There has been some attempt to equate boundary with belonging to some class or set. Zeleny and Hufford, for example, adopt this approach. They argue that social or categorical boundaries are readily distinguished

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and that restricting the deﬁnition to tangible boundaries ‘‘serves no useful purpose’’ (1991, p. 322). Mingers, in addressing this point, states: A physical boundary has a spatial dimension forming a barrier between inside and outside. This is not the case for a membership-type boundary; some members are not nearer the outside than others. (Mingers, 1995, p. 128)

Thus, in Mingers’ view, this substitution is unsatisfactory. Replacement of a physical boundary with a categorical distinction is substitution of a different equivalence class. A categorical distinction has no necessary operational basis or topological characteristics. Members are identiﬁed as related through a shared characteristic; there is no implied relation either spatially or through identiﬁcation of functional interdependence. Mingers’ reference to topology is interesting and important. It points to unexpected areas of ambiguity in the concept of boundedness, even for physical systems. Hejl (1984) notes, for example, that problems of boundary identiﬁcation are not conﬁned to social systems but are already present with biology. His point can be appreciated by considering the following questions: Are the inside membranes of the lungs, esophagus, and intestines ‘‘inside’’ or ‘‘outside’’ of the human body? Is the boundary of a pond ecology the waters edge? When considering the boundaries of social systems he concludes that as a system’s boundary is ‘‘constituted through the interactions of the components’’ (1984, p. 72), and as individuals are nodes in many intersecting social systems, and further, as the observer needs to be included in this ‘‘network’’ of intersecting social systems, then: it is not enough to deﬁne [the boundary] as an external observer. If we want to know where the boundaries of what we take hypothetically as a social system are, we have to observe as well as ask the individuals who constitute it. (Hejl, 1984, p. 72)

This is highly suggestive of naturalistic enquiry. The cases we have presented here involved a degree of this in that the methods stayed close to the language usage of the contributors, and they were involved in the choice of anecdotes. In the second case the natural language was again used in order to ﬁnd evidence of points of relative closure. In social systems then, boundaries are deﬁned by observers and it matters where we draw them. This is not to say that we cannot gain some empirical clues as to where we may usefully draw them and the Wikipedia case provides an example of the type of data that may be used for this purpose and the implications of drawing it in different places.

5 Overall Conclusions The ﬁrst case suggests that it may be possible to map the key distinctions which characterize and contribute to the coherence of particular linguistic domains. The challenge is in gaining sufﬁcient initial lead to know where to look closely. We have identiﬁed several conventional tools which can be used. Elsewhere we have also outlined a model to assist with the interpretation of the resulting ﬁndings (Goldspink et al., 2008). These are relatively easy to use and have modest data needs — a great

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deal can be gleaned from a well-targeted and small-scale collection of information from a few key players as was illustrated in the case. Doing so could prove highly valuable in a wide range of high-value organizational change exercises. These include mergers and acquisitions, structural change programs, and cultural realignment exercises. It demonstrates that the abstract theory being derived from autopoiesis can be used to signiﬁcant pragmatic effect and in real time in organizational transformation exercises. The analysis provides insights into the drivers of particular patterns and provides some clues to (a) the feasibility of attempting to change them and the likely scale of intervention needed and (b) where best to target any intervention so as to increase the likelihood of an outcome which has some similarity to that which was intended. The second case is interesting for an altogether different reason. It too provides practical understanding in this case of the governance processes associated with a new form of organization and how they are inﬂuenced by technical and social artifacts. It also demonstrates how conventional methods may be employed, although in this case the analysis was time consuming and not feasible for dynamic interpretation of events. The main contribution of this case therefore remains theoretical. It helps answer a long-standing controversial question among users of autopoietic theory: Are social systems (including organizations) autopoietic? Despite protestations to the contrary, autopoiesis and complexity theory can provide practical tools and guidance to real-world organizational and social problems. In combination they offer an opportunity to move past many of the long-standing problems of alternative social and organizational theory which are largely based on the assumption of change as the exception rather than the norm (Burrell & Morgan, 1980). They provide a means for directly theorizing about and, perhaps more importantly, researching and responding managerially to the interplay or dialectic between micro and macro level phenomena which are constitutive of organizational phenomena. While the theory we have been developing is not yet complete or fully articulated and while techniques which could see it applied most directly (such as multi-agent modeling) are still underdeveloped, we have demonstrated here that it can still be put to use. We have also argued that the greatest potential lies in working with the original conception developed by Maturana and Varela not on the grounds that it was complete and inviolate, but on the grounds that it offers a critical foundation otherwise lacking in social theory: an answer to the question ‘‘in what way is human social behavior derived from and constrained by our biology?’’ Excellent work is underway to advance and consolidate this foundation without needing to violate the essential premises upon which the original theory was based. We refer here to the work being undertaken in neurophenomenology (Thompson & Varela, 2001; Rudrauf et al., 2003; Thompson, 2004), artiﬁcial life (Moreno & Etxeberria, 1995; Moreno et al., 1997; Barandiaran, 2005; Barandiaran & Moreno, 2006; Duijn et al., 2006), and robotics (De Jaegher & Di Paolo, 2007; Di Paolo & Lizuka, 2007; Di Paolo et al., 2007). To attempt to redeﬁne autopoiesis to make it ﬁt with the constitutively different nature of social systems is not necessary, and moves to decouple it from its grounding in biology serve to weaken its application. In Luhmann, for example,

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the issue of domain intersection is highly problematic. It represents a retreat from coming to terms with the fundamental origins of social behavior in the cognitive capability of living breathing human agents.

References Bannister, D., & Fransella, F. (1989). Inquiring man: The psychology of personal constructs. London: Routledge. Barandiaran, X. (2005). Behavioral adaptive autonomy. A milestone on the ALife route to AI? San Sebastian, Spain: Department of Logic and Philosophy of Science, University of the Basque Country. Barandiaran, X., & Moreno, A. (2006). On what makes certain dynamical systems cognitive: A minimally cognitive organization program. Adaptive Behavior, 14(2), 171–185. Becker, B., & Mark, G. (1997). Constructing social systems through computer mediated communication. Sankt Augustin, Germany: German National Research Center for Information Technology. Browning, L., & Boudes, T. (2005). The use of narrative to understand and respond to complexity: A comparative analysis of the Cyneﬁn and Weikian models. E:CO, 7(3–4), 7. Bruner, J. (1991a). The narrative construction of reality. Critical Enquiry, 18(1), 20. Bruner, J. (1991b). Self-making and world making. Journal of Aesthetic Education, 25(1), 11. Burrell, G., & Morgan, G. (1980). Sociological paradigms and organizational analysis. London: Heinemann. Castelfranchi, C. (1998). Simulating with cognitive agents: The importance of cognitive emergence. In: J. S. Sichman, R. Conte & N. Gilbert (Eds), Multi-agent systems and agent based simulation. Berlin: Springer. Clayton, P., & Davies, P. (2006). The re-emergence of emergence: The emergentist hypothesis from science to religion. Oxford: Oxford University Press. Cosmelli, D., et al. (2007). Neurodynamics of consciousness. In: P. D. Zelazo, M. Moscovitch & E. Thompson (Eds), The Cambridge handbook of consciousness. Cambridge: Cambridge University Press. Dautenhahn, K. (2002). The origins of narrative: In search of the transactional format of narratives in human and social animals. International Journal of Cognition and Technology, 1(1), 26. Davies, P. (2006). The physics of downward causation. In: P. Clayton & P. Davies (Eds), The re-emergence of emergence: The emergentist hypothesis from science to religion. Oxford: Oxford University Press. De Jaegher, H., & Di Paolo, E. A. (2007). Participatory sense-making: An enactive approach to social cognition. Phenomenology and the Cognitive Sciences, 6(4), 485–507. Di Paolo, E. A., & Lizuka, H. (2007). How (not) to model autonomous behavior. Biosystems, 91, 409–423. Di Paolo, E. A., et al. (2007). Horizons for the enactive mind: Values, social interaction and play. In: J. Stewart, O. Gapenne & E. A. Di Paolo (Eds), Enaction: Towards a new paradigm for cognitive science. Cambridge, MA: MIT Press. Duijn, M. V., et al. (2006). Principles of minimal cognition: Casting cognition as sensorimotor coordination. Adaptive Behavior, 14(2), 157–170.

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Ellis, G. F. R. (2006). On the nature of emergent reality. In: P. Clayton & P. Davies (Eds), The re-emergence of emergence: The emergentist hypothesis from science to religion. Oxford: Oxford University Press. Fransella, F., et al. (2004). A manual for repertory grid technique. Chichester: Wiley. Fransella, F. B. (1977). A manual of repertory grid technique. London: Academic Press. Fuchs, C., & Hofkirchner, W. (2005). The dialectic of bottom-up and top-down emergence in social systems. TripleC, 1(1), 22. Gaines, B. R. (1981). Autopoiesis: Some questions. In: M. Zeleny (Ed.), Autopoiesis: A theory of living organization. New York, NY: North Holland. Goldspink, C. (2007). Normative self-regulation in the emergence of global network institutions: The Case of Wikipedia. ANZSYS07, Auckland, New Zealand. Goldspink, C., & Kay, R. (2003). Organizations as self organizing and sustaining systems: A complex and autopoietic systems perspective. International Journal General Systems, 32(5), 459–474. Goldspink, C., & Kay, R. (2004). Bridging the micro–macro divide: A new basis for social science. Human Relations, 57(5), 597–618. Goldspink, C., & Kay, R. (2007). Social emergence: Distinguishing reﬂexive and non-reﬂexive modes. AAAI Fall Symposium: Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence, Washington. Goldspink, C., & Kay, R. (2008). Agent cognitive capability and orders of emergence. In: G. Trajkovski, & S. Collins (Eds.), Agent-based societies: Social and cultural interactions. Hershey, PA: Information Science Reference. Goldspink, C., et al., (2008). Organizational change: Revealing the micro–macro patterns underlying social system dynamics in a ﬁnancial services context. First ICC Workshop on Complexity in Social Sciences, ISCTE Lisbon, Portugal. Gotham, K. F., & Staples, W. G. (1996). Narrative analysis and the new historical sociology. The Sociological Quarterly, 37(3), 20. Grice, J. W. (2002). Idiogrid: Software for the management and analysis of repertory grids. Behavior Research Methods, Instruments, and Computers, 34, 3. Habermas, J. (1976). Some distinctions in universal pragmatics: A working paper. Theory and Society, 3(2), 12. Hejl, P. (1981). The deﬁnition of system and the problem of the observer: The example of the theory of society. In: G. S. Roth (Ed.), Self-organizing systems: An interdisciplinary approach (pp. 171–185). New York, NY. Hejl, P. (1993a). Culture as a network of socially constructed realities. In: A. F. Rigney (Ed.), Cultural participation: Trends since the middle ages (pp. 227–250). Amsterdam: John Benjamins Publishing Company. Hejl, P. M. (1984). Towards a theory of social systems: Self-organization, self-maintenance, self-reference and syn-reference. In: P. Ulrich (Ed.), Self-organization and management of social systems: Insights, promises, doubts and questions (pp. 60–78). Berlin: Springer-Verlag. Hejl, P. M. (1993b). Culture as a network of socially constructed realities. In: A. Rigney & D. Fokkema (Eds), Amsterdam: John Benjamins Publishing Company. Jankowics, D. (2004). The easy guide to repertory grids. London: Wiley. Kelly, G. A. (1963). A theory of personality. New York, NY: Norton. Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press. Landﬁeld, A. W., & Cannell, J. E. (1988). Ways of assessing functionally independent construction, meaningfulness, and construction in hierarchy. In: J. C. M. M. L. Shaw (Ed.), Cognition and personal structure (pp. 67–90). New York, NY: Praeger.

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Maturana, H. (1988a). The ontology of observing: The biological foundations of selfconsciousness and the physical domain of existence. Felton, CA: American Society for Cybernetics Conference. Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: The realization of the living. Boston, MA: D. Reidel. Maturana, H. R. (1988b). Reality: The search for objectivity of the quest for compelling argument. Irish Journal of Psychology, 9(1), 25–82. Maturana, H. R., & Varela, F. J. (1992). The tree of knowledge: The biological roots of human understanding. Boston, MA: Shambhala. Mingers, J. (1991). The cognitive theories of Maturana and Varela. Systems Practice, 4(4), 319–338. Mingers, J. (1995). Self-producing systems: Implications and applications of autopoiesis. New York, NY: Plenum. Moreno, A., & Etxeberria, A. (1995). Agency in natural and artiﬁcial systems. San Sebastian, Spain: Department of Logic and Philosophy of Science, University of the Basque Country. Moreno, A., et al. (1997). Cognition and life. Brain and Cognition, 34, 107–129. Rocha, L. M. (1996). Eigenbehavior and symbols. Systems Research, 13(3), 371–384. Rudrauf, D., et al. (2003). From autopoiesis to neurophenomenology: Francisco Varela’s exploration of the biophysics of being. Biological Research, 36, 27–65. Sanger, L. (2005). The early history of Nupedia and Wikipedia: A memoir. Slashdot, http:// features.slashdot.org/article.pl?sid ¼ 05/04/18/164213 Sawyer, K. R. (2001). Emergence in sociology: Contemporary philosophy of mind and some implications for sociology theory. American Journal of Sociology, 107(3), 551–585. Sawyer, K. R. (2003). Artiﬁcial societies: Multiagent systems and the micro–macro link in sociological theory. Sociological Methods and Research, 31, 38. Sawyer, K. R. (2005). Social emergence: Societies as complex systems. Cambridge: Cambridge University Press. Searle, J. R. (1969). Speech act: An essay in the philosophy of language. Cambridge: Cambridge University Press. Snowden, D. (2000). New wine in old wineskins: From organic to complex knowledge management through the use of story. Emergence, 2(4), 14. Snowden, D. (2001). Narrative patterns. Knowledge Management, 4(10). Stiles, W. B. (1992). Describing talk: A taxonomy of verbal response modes. London: Sage. Taplikis, T. (2005). After managerialism. E:CO, 7(3–4), 2–14. Thompson, E. (2004). Life and mind: From autopoiesis to neurophenomenology, a tribute to Francisco Varela. Phenomenology and the Cognitive Sciences, 3, 381–398. Thompson, E., & Varela, F. J. (2001). Radical embodiment: Neural dynamics and consciousness. Trends in Cognitive Sciences, 5(10), 418–425. Tsoukas, H., & Chia, R. (2002). On organizational becoming: Rethinking organizational change. Organization Science, 13(5), 567–582. van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21, 615–665. Weik, E. (2006). Working relationships: A meta view on structure and agency. Theory and Science, 7(1). Zeleny, M., & Hufford, K. D. (1991). All autopoietic systems must be social systems: An application of autopoietic criteria in systems analysis. Journal of Social and Biological Structures, 14(3), 311–332.

Chapter 6

Autopoiesis and Critical Social Systems Theory Christian Fuchs and Wolfgang Hofkirchner

1 Introduction Maturana and Varela (1980, p. 78f) provided the following deﬁnition of autopoiesis: ‘‘An autopoietic machine is a machine organized (deﬁned as a unity) as a network of processes of production (transformation and destruction) of components that produces the components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them and (ii) constitute it (the machine) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network.’’ This deﬁnition shows that for Maturana and Varela, autopoietic systems are systems that deﬁne, maintain, and reproduce themselves. The notion of machine that they employ in the deﬁnition might seem a bit misleading because we tend to think of machines as mechanistic and nonliving, but Maturana and Varela (e.g., 1987) in later publications have preferred to speak of autopoietic organizations. Social systems are systems that are based on the interactions of living systems. Maturana considers them as higher-order systems. The question therefore arises if these systems are also autopoietic systems. The paper at hand will discuss this question and try to give an answer that is critical of the one given by the main representative of the theory of social autopoiesis — Niklas Luhmann. According to Niklas Luhmann, the ﬁrst and still most prominent thinker on social autopoiesis, organizations are a variety of social systems besides interaction systems and societal systems. As there has been much discussion on the question whether social systems in general can be said to be autopoietic and if so to what extent, we resume this discussion. The rise of the importance of the sciences of complexity can be interpreted as a turn toward the conception of reality as complex, dynamic, and networked. In biology, Maturana and Varela (1987) have been two of the most important

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scholars who are well known for the application of complexity thinking to living systems. They argue that the differentia speciﬁca of living systems is that they can maintain and reproduce themselves by dynamically producing their own components and with them a systemic unity. Conceiving a system as autopoietic means to stress that it is dynamic and self-creating. The question arises if it is possible to generalize this concept and to apply it to social systems and what advantages or disadvantages such an endeavor brings. In this paper, we discuss two basic possibilities for considering social systems as self-producing systems. First, we discuss Niklas Luhmann’s approach of self-referential systems, which can be considered as the most important approach of autopoietic social theory. Second, based on a critique of Luhmann, we introduce an alternative approach that we term critical social systems theory.

2 Niklas Luhmann’s Theory of Social Systems Maturana has deﬁned a social system as ‘‘a collection of interacting living systems that, in the realization of their autopoiesis through the actual operation of their properties as autopoietic unities, constitute a system that as a network of interactions and relations operates with respect to them as a medium in which they realize their autopoiesis while integrating it, is indistinguishable from a natural social system and is, in fact, one such system’’ (Maturana, 1980, p. 11; Maturana, 1987, p. 292). The focus is on individuals, interactions, and networks. However, Maturana argues that social systems are conservative, non-autopoietic systems.1 This assessment could reﬂect the difﬁculty in arguing what the autopoietic unity that is permanently reproduced is in the case of social systems. Human individuals are not permanently created because their creation is a singular biological process that starts life. Niklas Luhmann has attempted to interpret social systems as self-producing by taking a nonhuman-centered approach. Luhmann (1984) conceives society in rather functional terms, wants to apply Maturana’s and Varela’s autopoiesis concept sociologically, and sees social systems as self-referential systems with communications as its elements. He says that a system can only differentiate itself if it refers to itself and its elements. It generates a description of itself and a difference between system and environment. Selfobservation means that a system/environment difference is introduced into the system. All social systems can observe themselves. Luhmann argues that individuals are (re)produced biologically, not socially. This means that for Luhmann biological reproduction by sexual intercourse is the reproduction mechanism of humans, but not the reproduction mechanism of social systems. He infers from this argument that humans are not part of social systems and that human actors cannot be the components of social systems. If one wants to consider a social system as autopoietic or self-referential, the permanent 1

http://www.inteco.cl/biology/ask9707-1.htm

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(re)production of the elements by the system is a necessary condition. Hence, Luhmann says that not individuals but communications are the elements of a social system. A communication results in a further communication; by the permanent (re)production of communications a social system can maintain and reproduce itself. ‘‘Social systems use communications as their particular mode of autopoietic reproduction. Their elements are communication which are recursively produced and reproduced by a network of communications and which cannot exist outside such a network’’ (Luhmann, 1988, p. 174). For Luhmann, human beings are sensors in the environment of the system. He says that the ‘‘old European humanistic tradition’’ conceives humans within and not on the outside of social systems. Systems theory would have no use for the subject and the human being could not be the measure/ standard of society. Luhmann stresses communicative processes instead of human beings. The ‘‘revolution’’ in social science that he wanted to bring about is one that conceptually excludes human actors from society. Luhmann resolves the sociological problem of how social structures and human actors are related dualistically, which results in inconsistencies and theoretical lacks. According to some critics of Luhmann, he fails to explain how one communication can exactly produce other communications without individuals being part of the system: ‘‘There is no signiﬁcant attempt to show how societal communication y emerges from the interactions of the human beings who ultimately underpin it. Without human activity there would be no communication y . It is one thing to say analytically that communications generate communications, but operationally they require people to undertake speciﬁc actions and make speciﬁc choices y . One communication may stimulate another, but surely it does not produce or generate it’’ (Mingers, 1995, p. 149f). An autopoietic conception of society must show consistently how society produces its elements and thereby reproduces itself. Beyerle (1994, 137f) criticizes that Luhmann does not show how communications are produced. Luhmann only mentions that communications result in further communications. He can explain that society is self-referential in the sense that one communication is linked to other ones, but he can’t explain that it is self-producing or autopoietic. In Luhmann’s theory, not humans but only social systems act; he describes systems in human terms and neglects human agency. There is one characterization and critique that Giddens (1984) gives of functionalism that also holds for Luhmann’s social systems theory: functionalism is unable to see human beings as reasoning, knowledgeable agents that with practical consciousness that are at the center of social reproduction and argues that society and institutions have needs and fulﬁll certain functions. Luhmann considers humans as being outside observers of social systems and he assigns certain functions to subsystems of a functionally differentiated society. Functionalism according to Giddens (1984) sometimes results in views of a subjectless history that is driven by forces outside the actors’ existence that they are wholly unaware of. Reading Luhmann, one gets the impression that systems are for him such outside forces. That Luhmann does not see humans as part of social systems is indicative of this. Luhmann’s thinking dichotomizes the social and the individual by paying almost no attention to individual social action that draws on or is shaped by social structure and, in turn, reproduces such a structure.

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The consequence of Luhmann’s exclusion of humans and their interests from his theory is a blindness for social problems that created an afﬁrmative uncritical theory that describes society as it is, not also as it could be. Luhmann (1984) explicitly argues that his theory is not a social problems approach. So Luhmann (1996a), e.g., claims that the mass media can’t manipulate humans because they, just like every system, would construct a legitimate reality. The function of the mass media for him is that they provide topics for communication and hence advance the autopoiesis of society. There is no analysis of simpliﬁcation, scandalization, and emotionalization as media tactics, one-dimensional reporting, staged media events, the role of the Internet in the mass media, media monopolies, and so on. For Luhmann, there are no problematic aspects of the mass media — and of contemporary society at a whole. The dramatic implications of Luhmann’s theory become most apparent in his discussion of protest movements. He argues that social movements are alternatives without alternatives (Luhmann 1996b, p. 75ff.), that they protest against the functional differentiation of society (p. 76), operate within society against society (pp. 103, 204), have no alternatives to offer (p. 104), fetishize opposition and alternative thinking (p. 159), are made up by a notoriously mentally instable public (p. 204), stage provocation as end in itself (p. 206), possess no analytical depth and don’t know why something is as it is (p. 207), stage protest as pseudoevents (p. 212), are a form of refractory communication against communication (p. 214), constitute a disturbing aspect of modern society (Luhmann 1984, p. 545), and act as negators that weaken the afﬁrmation of society (ibid., p. 549ff.). For Luhmann, protest movements are reactive, aimlessly, and dangerous. Each protest movement has values and certain political goals; hence, it wants to change society. Social movements are not reactive but active and proactive. Luhmann’s characterization aims at discrediting protest; if the latter is not seen as a positive function of society, alternatives are considered as undesirable. A society that forestalls critique seems close to a totalitarian society; a theory that considers critique and opposition as undesirable is afﬁrmative and seems accordingly close to a totalitarian theory. The role of sociology in society is critique and reﬂection of society; a pure description of society as it is as the best form of society is uncritical and afﬁrmative. For Luhmann, the function of protest movements is that they convert the negation of society in society into operations (ibid., p. 214). According to Hegel, a contradiction can be interpreted as not purely negative but a determinate negation, i.e., a contradiction results in the negation of the negation; it is sublated and produces positive results. Protest movements then can be considered as a negation of existing structures and values, but they strive for changing society, i.e., for a negation of the negation and for sublation. They are movements because they move society and want to guarantee dynamic change. Based on a dualistic concept of system and environment, Luhmann can neither explain how ecological problems are caused nor how they could be solved; he is only interested in how society communicates about ecological problems (ecological communication) and argues that ecological problems are only problems because society communicates them as problems (Luhmann, 2004, p. 63), which suggests a radical constructivist perspective that doubts the existence of real problems. In such an approach, ecological problems are not real but only constructed. The Habermas/Luhmann debate has shown that there is a difference between critical thinking and the thinking of Luhmann (Habermas & Luhmann, 1971). Habermas’s

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main criticism of Luhmann is that the latter considers society as instrumental and describes it as it is and not as it could be. Luhmann is only interested in describing society, whereas Habermas argues that ignoring social problems and aspects of how to improve society and how to advance human interests and human emancipation means to reduce sociology to the logic of instrumental and functional reason. Habermas says that Luhmann ignores the intersubjective and democratic dimensions of social relationships, i.e., that consensus and participation can be achieved by communicative action in ideal speech situations that satisfy the four validity claims of truth, truthfulness, rightness, and comprehensibility. Luhmann argues that modern society is too complex for allowing discursive decision taking. It is no wonder that based on a system/human dualism, he is blind for social problems and human interests. Luhmann (1984, p. 114) argues in this context that he does not pursue a social problems approach. We agree with Habermas’s criticism in this respect. Luhmann constituted a methodological antihumanism, whereas critical theories have always been forms of methodological humanism. Critical theory is about analyzing how to change society, for Luhmann social theory is about describing society. This is a crucial difference. We stick to Horkheimer’s view that theory should not have an interest in ‘‘the preservation of contemporary society but in its transformation into the right kind of society’’ (Horkheimer, 1937/2002, p. 218). Such theories try to show conditions and hindrances for the emergence of a ‘‘society without injustice’’ (221) that is shaped by ‘‘reasonableness, and striving for peace, freedom, and happiness’’ (222), ‘‘in which man’s actions no longer ﬂow from a mechanism but from his own decision’’ (229), and that is ‘‘a state of affairs in which there will be no exploitation or oppression’’ (241). An alternative view of how autopoiesis can be applied to social systems is the critical social systems theory approach.

3 Critical Social Systems Theory A critical social systems theory is a critical theory of social systems. It combines the stance of critical theory as represented by, e.g., Habermas and Herbert Marcuse and the Frankfurt School philosophers like Ernst Bloch — a theory which has its roots in the weltanschauung of Karl Marx — and a system theoretical view, in particular, science of complexity insights provided by Evolutionary Systems Theory (EST) applied to the domain of social systems and going back to General System Theory (GST) as inaugurated by Ludwig von Bertalanffy among others. We present this approach by discussing three aspects of critical social systems: design, modeling, and methodology one by one.

3.1

Critical Social Systems Design: The Importance of Being Critical

Design is concerned with the relation of theory and technology, theory and practice. It’s the context of application, in which scientiﬁc knowledge is used for solving problems and is transformed into technologies, whether material or ideational. It addresses the opposition of normative versus descriptive.

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This question had been contested with considerable amount of attention in the second half of the last century. Positivism tried to exclude this context by terming it a factor external to science. Nowadays, in social science there seems to be a consensus on rejecting the ideology of value-free science. Not so with Luhmann. Luhmann’s theory is nonnormative, i.e., it avoids to discuss and criticize societal problems. By doing so, however, it becomes afﬁrmative because just describing society as it is means to leave it unquestioned and give dominant groups the opportunity to positively refer to this theory in their endeavor to uphold asymmetric power relations. Critical thinking is not entirely new to systems theory. If we equate the beginnings of systems science with Ludwig von Bertalanffy’s GST, then systems science has been normative from its very beginning. Bertalanffy’s GST is a humanistic one. Thus all his descriptions of humans and social systems serve the function to help to formulate guidelines for acting toward humane norms and values (see Hofkirchner, 2005). Approaches like Critical Systems Thinking (CST) have been grounded in Habermas’ version of critical theory. Two of the ﬁve commitments of CST are critical awareness and dedication to human emancipation (Jackson, 2003). CST rests ‘‘upon Habermas’ theory of human interests as mediated through the system of system methodologies’’ (Jackson, 2003, p. 83). CST is ‘‘dedicated to human emancipation and seeks to achieve for all individuals the maximum development of their potential’’ (ibid., p. 85). It especially tries to advance the emancipatory interest (which is one fundamental human interest besides the technical and the practical interest) of humans by ‘‘denouncing situations where the exercise of power, or other causes of distorted communication, are preventing the open and free discussion necessary for the success of interaction’’ (ibid., p. 85). CST sees itself in the service of a more general emancipatory project (ibid., p. 86). ‘‘Critical systems thinking, and the thrust of Total Systems Intervention (TSI) therefore, is emancipatory in that it seeks to achieve for all individuals, working through organizations and in society, the maximum of their potential. (y) The exercise of power in the social process can prevent the open and free discussion necessary for the success of interaction. Human beings have, therefore, an ‘emancipatory’ interest in freeing themselves from constraints imposed by power relations and in learning, through a process of genuine participatory democracy, involving discursive will formation, to control their own destiny’’ (Flood & Jackson, 1991, p. 95f). Critical social systems thinking can easily be based upon EST — a term by which a theory of complex, dynamic, nonlinear, open, self-organizing systems is denoted. Evolutionary systems design principles encourage to make use of the systems’ dynamic and stress the point that knowing about nonlinearity and sensitivity may help to choose those inputs that trigger developments in the overall self-organization process of the system that are favorable to those who make the inputs. System processes may be facilitated or may be dampened. Also it is important to inﬂuence the general setup of the system only and abandon instructions down to every detail so that relative autonomy is granted to the subsystems. Being critical can be ascribed to this theoretical framework when applied to social systems in that it is normative while

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doing justice to the factual at the same time. For it includes not only an account of the potential that is given with the actual, but also an evaluation of the potential which sorts out the desired. In a philosophical perspective, this deliberate activism is not a practicism that guides action according to the maxim that all that is feasible shall be realized thereby assuming that it is desired too. Nor is this kind of activism a utopian or romantic wishful thinking that holds that what is desired is feasible too. Both practicism and wishful thinking believe in total controllability and result in expensive brute-force interventions. Nor is this kind of activism an inactivism that believes in total uncontrollability, condemns any kind of intervention and fails to reconcile the feasible and the wishful. On the contrary, it takes responsibility for producing the unity of the feasible and the wishful. And it does so by working out the ascendance from the potential given now to the actual to be established in the future as well as the ascendance from the less good now to the better then which altogether yields the Not-Yet in critical theorist Ernst Bloch’s sense (see, e.g., Bloch, 1967). These processes aimed at the Not-Yet are at the core of the dynamic of social selforganization. By the notion of the Not-Yet Bloch tried to salvage the idea of utopia — it is not any longer a nowhere deprived of the possibility to get there but a future that can be glimpsed and anticipated in what is already possible here and now. Why is it especially important today to advance a critical approach? We consider it irresponsible if social theory is watching as a bystander as the world is increasingly getting out of human control. Due to the existence of global problems, we argue that a critical social systems theory is needed. There is evidence that late-modern society is characterized by culminating antagonisms between economic feasibility and social usefulness of technological products, between economic growth and ecological sustainability, and between economic freedom (of markets) and social equity.2

2

Income inequality measured as the relation of the mean income of the upper and the lower quintile has decreased in the years 1995–2000 in the EU15 countries, but it has increased from 4.5 in 2000 to 4.8 in 2005 (Eurostat Online). The higher this measure, the higher the income disparity between the poorest and the richest. In the EU25 countries, it has increased from 4.5 in 2000 to 4.9 in 2005. In 2000, the richest 5% Europeans owned 35.7% of the worldwide wealth (Davies et al., 2006, table 10a). The at-risk-of-poverty rate after social transfers measured by 60% of median equivalized income after social transfers has risen from 15% in 1998 to 16% in 2005 in the EU15 as well as the EU25 countries (Eurostat Online). Income inequality as measured by the Gini coefﬁcient has increased from 29 in 1998 to 31 in 2005 in the EU25 countries and from 29 in 1998 to 30 in 2005 in the EU15 countries (Eurostat Online). The in-work at risk of poverty rates for part time workers was 11% in the EU25 and 10% in the EU15 countries in 2005 (Eurostat Online). The increase in income inequality, job insecurity, and poverty risk has been accompanied by a polarization between capital and labour: Whereas the average proﬁt rate has increased by 39.4% in the years 1987–2007 in the EU15 countries (net returns on net capital stock, European Commission Annual Macro-Economic Database), the wage share has in the same time span decreased by 7.5% (Compensation per employee as percentage of GDP at current market prices, European Commission Annual MacroEconomic Database). It is hence reasonable to assume that during the last years and decades, economic growth has been accompanied by a rise of relative wage decreases, income inequalities, and poverty risks. Hence, we assume that such a form of economic growth, i.e., the unhindered expansion of capital accumulation, is not compatible with social sustainability.

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Critical theory doesn’t accept existing social structures as they are, it is not interested in society as it is, but in what society could be and can become. It deconstructs ideologies that claim that something can’t be changed and shows potential counter tendencies and alternative modes of development. That the negative antagonisms are sublated into positive results is not an automatism, but depends on the realization of practical forces of change that have a potential to rise from the inside of the systems in question in order to produce a transcendental outside that becomes a new whole. All critical approaches in one or the other respect take the standpoint of oppressed or exploited classes and make the judgment that structures of oppression and exploitation beneﬁt certain classes at the expense of others and hence should be radically transformed by social struggles. We understand the notion of critical theory in the sense of approaches that are oriented on maximizing human potentials and realizing societal conditions that give advantages to all humans. Such theories are human-centered; they have human needs and the goal of a good life for all as their central concern. This endeavor also includes criticizing societal conditions that limit human potentials as unjust. If critical theory means human-centeredness as normative quality, then such a theory needs to put humans also in the center of theory itself. That global problems like global war, the ecological crisis, rising inequality, precarious labor and living conditions, etc., have emerged is an indication for the assumption that under the given societal conditions, human-centeredness is only the essence, but not the reality of society — to paraphrase Hegel. Not all humans beneﬁt, only certain classes beneﬁt at the expense of the large majority. Human-centeredness implies that society shall be designed in a way that allows all humans to realize a maximum of their potentials and to live a good life. As this is not the case today, human-centeredness implies the critique of contemporary society and the normative claim for societal transformation. Contemporary capitalist society is not human-centered, but capital- and powercentered — money capital and political power have colonized human interests and caused an alienation of society from its human essence. Our approach therefore aims at human-centeredness, but at analyzing social systems nonetheless as dynamic and self-producing. What is the basic advantage of the application of a transposed notion of autopoiesis to social systems for a critical theory? If social systems are conceived as dynamic, fundamental social change can be conceived as a potential development. This is particularly important today because neoliberal scholars and politicians tend to argue that there are no alternatives to neoliberalism (the ideology of ‘‘there is no alternative,’’ TINA) in particular and capitalism in general. Dynamics means that change is an inherent feature of society. If change is taking place permanently, then it is likely that fundamental change can also occur and is an option that humans can pursue. This holds not only for societal systems, but for organizations as well.

3.2

Critical Social Systems Modeling: Social Change as Autopoiesis, Autopoiesis as Self-Organization

The basic onto-epistemological question is: are models constructs that are subjective, kind of arbitrary, and can’t be corroborated because of the lack of an authority that

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would decide upon truth or untruth because this decision, in turn, would need a legitimation — and so forth ad inﬁnitum — or are models made for mapping reality by which objectivity would enter the scene? The answers distinguish between a constructivist and a realist stance. Modeling is about the relation of theory to reality and by that about the relations of theories to each other. It is the context of justiﬁcation in which scientiﬁc knowledge is critically exposed to possible refutations and corroborated in as far as it is not refuted and theories are comparatively assessed. Unlike today’s radical constructivism, Bertalanffy’s GST supported the idea that we are dealing with real-world systems and not with mere constructs. However, there is also a constructivist part in his GST perspective, for he appreciated the fact that it is models we construct in dealing with reality and that it is models that determine how we perceive reality. He called his view ‘‘perspectivism’’ which is neither absolutism nor nihilism. He stated that, e.g., a ﬂy, a dog, or a human being has only limited knowledge of the world, but that this knowledge has some validity because otherwise the ﬂy, the dog, the human would not have been able to survive for long (see Hofkirchner, 2005). Evolutionary systems modeling principles take as starting point that selforganization takes place in phases that yield different levels of real-world systems. Evolutionary systems undergo stages. The stage model of systems evolution is based upon the principle of emergentism and the principle of asymmetrism. Emergence takes place in transitions in which by the interaction of proto-elements systems are produced. Asymmetry describes the suprasystem hierarchies in which subsystems are encapsulated. The ontological perspective of EST — a term coined by Ervin Laszlo (1987), Vilmos Csanyi (1989), and Susantha Goonatilake (1991) — as a theory about evolving systems is the result of the merger of systems theory and evolutionary theory which nowadays not only applies to living and human/social systems but also to physical systems, i.e., to the cosmos itself. EST aims at distinguishing between different levels of self-organization, i.e., selforganization has aspects that are common to all types of systems as well as aspects that are unique to a concrete type of system. There are systems and processes that manifest patterns. Pattern is form, i.e., a superstructure that refers to a basis that refers to the superstructure, and so on. These are macro- and micro-levels that coexist and inﬂuence each other which is more important than the inﬂuence from outside. The system is produced by its elements, and the system constrains and enables its elements at the same time. As this works by dissipation of entropy, Ilya Prigogine (1980) called the emerging structures ‘‘dissipative.’’ The ﬂuid particle in the Be´nard convection cell — i.e., a hexagonal pattern emerging from conduction in liquids of high viscosity if exposed to a temperature gradient that exceeds a certain critical value — is prompted to contribute to the cell structure that emerges from the activities of all particles. This is said to be true of all self-organizing systems on a physical and chemical level. Then there are systems and processes that are able to maintain the form they show, i.e., to hold the form stable while matter is changing. This is the case with all living systems. This is why Maturana and Varela (1980) called them ‘‘autopoietic.’’

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By stressing the fact that in living systems the elements that constitute the system produce new elements by which the system can be constituted, Maturana and Varela denoted that living systems are systems that produce themselves by constraining and enabling their elements to produce new elements that produce the systems. Put it that way, it becomes clear that EST can consider autopoiesis, i.e., living self-organization, as evolutionary follow-up of dissipative self-organization in physical and chemical systems and as physical and chemical basis for biotic self-organization processes. Autopoiesis can be looked upon as a particular way of universal self-organization, characteristic of living systems. The same ﬁgure of thinking evolutionary systemically is applied by EST when it comes to the human/social level. Human/social system and processes are viewed as systems and processes that change their form in a rather deliberative way, i.e., they are endowed with the capability to transcend themselves, invent themselves. This is what Erich Jantsch (1987) was pointing at when talking of ‘‘re-creative’’ systems at the human/social level. Re-creation means that social systems do not only have the capacity to modify themselves (as physical and chemical self-organizing systems do) and to essentially maintain themselves (as living self-organizing systems do), but they also have the capacity to reinvent themselves, to shape themselves, to produce a speciﬁc character by which the individuals that are parts of a social system can strive to realize themselves in a more or less self-determined way. That is to say, systems at the evolutionary stage of human society are just another — but new — way of metabolism nonhuman living systems carry out (just as systems at the evolutionary stage of living beings are another way of making use of energy than nonliving material systems do): re-creation is a particular way of autopoiesis which is a particular way of dissipative self-organization. In this vein, there is no problem to include human beings in social systems, but it is a requisite to do this. Though humans are ‘‘produced’’ by humans in a biological sense, they are also produced as social beings, as members of social systems, by the actions they carry out under the constraints and enablers social systems represents to them. So autopoiesis is clearly there, and it is amended in so far as transformations of social systems can occur. Applying EST models to social systems means to give an answer to how to relate individuals and society — the central theme each general sociological theory revolves around and which is known today as the duality of agency and structure (see Reckwitz, 1997). Given this basic duality, there are four ways of conceiving of their relationship. The ﬁrst way is individualism which can be classiﬁed as downward reductionism because it gives priority to individual phenomena over societal ones as action theory does. The second way is a kind of reverse reductionisms which better may be called downward projectivism typical of structuralism that reverses the priority relationship, which means that properties of the higher, the macro-, level (society) are projected onto, or extrapolated to, properties of the lower, the micro-, level (individuals and their actions). The third way is a dualistic view that takes for granted the independent existence of structures and agency and cuts

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individuals free from societal structures which is the way Luhmann chose (see Hofkirchner, 2006). The approach of critical social systems theory that we have tried to ground during the past years (cf., e.g., Hofkirchner, 1998, 2007; Fuchs & Hofkirchner, 2003, 2005; Fuchs, 2008a) is a fourth way to conceive of this relationship and starts from the human-centered argument that human beings as such are creative social beings that cocreate social reality together with others. Society is conceived as a large-scale system of networked social systems that is based on the dialectic of social structures and human actors. By social actions, social structures are constituted and differentiated. The structure of a social system is made up by the total of regularized social behavior and relations that are continuously reproduced over certain time spans. By social interaction, new qualities and structures can emerge that cannot be reduced to the individual level. This is a process of bottom-up emergence that is called agency. Emergence in this context means the appearance of at least one new systemic quality that cannot be reduced to the elements of the system. So this quality is irreducible and it is also to a certain extent unpredictable, i.e., time, form, and result of the process of emergence cannot be fully forecast by taking a look at the elements and their interactions. Social structures also inﬂuence individual actions and thinking. They constrain and enable actions. This is a process of top-down emergence where new individual and group properties can emerge. The whole cycle is the basic process of systemic social self-organization that can also be called re-creation because by permanent processes of agency and constraining/ enabling, a social system can not only maintain and reproduce itself but also transform itself, i.e., create itself anew (see Figure 1, Hofkirchner, 1998, cf. also Fuchs, 2008a). It again and again creates its own unity and maintains itself. Social structures enable and constrain social actions as well as individuality and are a result of social actions (which are a correlation of mutual individuality that results in sociality). We term social systems due to their dependence on human creativity and self-producing re-creative systems. This approach is dialectical because it conceives social systems as an interconnection of human actors and social structures. Actors and structures on the one hand are different, on the other hand actors form and are part of certain social structures and social structures condition and hence become part of human actions. The relationship can be conceived as being based on difference, unity, and interdependence. Individuals and society are interdependent (none of them can be understood without the other), they oppose each other (none of them is fully understandable by understanding the other), and they build a systemic hierarchy (society plays the dominant role). Dialectics is said to apply whenever two correlates build a mutually dependent relationship between themselves as opposites in an asymmetrical way. In Luhmann’s approach, the unit of social autopoiesis is communication. In our approach, the unit of social autopoiesis ( ¼ re-creation) is human actors permanently reproducing and/or transforming social structures. Society doesn’t produce and

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Figure 1: The dialectic of actors and social structures.

reproduce humans biologically, but as social beings. What is permanently created in society is the fundamental quality of humans, their sociality. Society reproduces and produces man as a social being, and man reproduces and produces society by socially coordinating human actions. Man is creator of, and created by, society; society and humans produce each other mutually. In this context, Luhmann’s theory of social systems seems to point to what constitutes the macro-level in our diagram (see Figure 1) only, to what is termed structure, while our approach gives a bigger picture and can be looked upon as an extension, complementation, relativation, and revision of his ideas what makes our approach a post-Luhmannian and partly anti-Luhmann one (Hofkirchner, 2006; Fuchs, 2008a). This is also what John Mingers insinuates

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when he opts for a synthesis of Luhmann’s theory with a more action-oriented approach.3 The notion of the dialectics of structures and actors can be found in some important contemporary dialectical social theories. The connection to the sciences of complexity is directly acknowledged by Roy Bhaskar and Margaret Archer, who use the complexity notion of emergence. In his Transformative Model of Social Activity, Bhaskar introduces the notion of the ‘‘dialectics of structure and agency’’: ‘‘social structure is a necessary condition for, and medium of, intentional agency, which is in turn a necessary condition for the reproduction or transformation of social forms’’ (Bhaskar, 1993, p. 153). Margaret Archer distinguishes between ‘‘people’s emergent properties’’ (PEPs), ‘‘structural emergent properties’’ (SEPs), and ‘‘cultural emergent properties’’ (CEPs). Her approach of Social Realism is based on the ‘‘dialectical relationship between personal and social identities’’ (Archer, 2002, p. 18), ‘‘a synthesis such that both personal and social identities are emergent and distinct, although they contributed to one another’s emergence and distinctiveness’’ (Archer, 2002, p. 18). Bhaskar and Archer understand society as the permanent emergence of structures based on human identity and activity. Autopoiesis, formulated with the help of the complexity notion of emergence, means a permanent emergence of components of systems through the interactions of these components. The permanent emergence of social reality that Bhaskar and Archer describe clearly has a parallel with the notion of autopoiesis, both share the stress of dynamics and self-production. The difference is that autopoiesis has been mainly used in constructivist theories. Hence, one can read Bhaskar’s and Archer’s approaches as a plea for a realist turn in the application of autopoiesis to society and social systems. Pierre Bourdieu and Anthony Giddens are two other scholars who have based their theories on the dialectic of structures and agency. However, they haven’t connected

3

Mingers’ theoretical works form a very important contribution to social theory because he tries to connect aspects of social self-organization with modern sociological theories. Mingers wants to combine Luhmann’s with Giddens’ theory and says that society is mutually related to the interactional domain where people interact. ‘‘Society selects interactions and interactions select society — this is their form of organizational closure. We can choose to observe society, and see networks of communications triggering further communications, and forming self-bounded subsystems that persist and reproduce over time. Or, we can focus on particular episodes of interaction between individuals and groups’’ (Mingers, 1999, p. 38). If one observes society or a social system, one will not ﬁnd either communications or interacting individuals, but both at once. Separating communications and individuals into two separate domains results in a rather dualistic and non-consistent conception. Communication and social interactions do not constitute separate domains, they are part of the structure that relates social groups and individuals, they exist in-between individuals as a connecting mechanism. To avoid shortcomings, one could conceive social structures as a unity of social relationships that take place in and through interaction and communication and social forms such as rules and resources. As long as communications are deﬁned as components of a social system, it is a very hard or nearly impossible task to integrate the theories of Luhmann and Giddens. We prefer to deﬁne individuals as social beings and components of social systems in such a way that society produces man as a social being just like man produces society as a necessary condition for his/her social being.

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their works to the sciences of complexity (for such an endeavor cf. Fuchs, 2003a, 2003b). Bourdieu argues that there is a ‘‘dialectical relationship between the objective structures and the cognitive and motivating structures which they produce and which tend to reproduce them, (y) these objective structures are themselves products of historical practices and are constantly reproduced and transformed by historical practices whose productive principle is itself the product of the structures which it consequently tends to reproduce’’ (Bourdieu, 1977, p. 83). For Bourdieu, the concept that establishes the connection between structures and agency is that of the habitus. Giddens formulates the dialectic as duality of structure: ‘‘According to the notion of the duality of structure, the structural properties of social systems are both medium and outcome of the practices they recursively organize’’ (Giddens, 1984, p. 25). One aspect that these approaches have in common is that they consider themselves as dynamic critical realist theories that are not naive, but dynamic and acknowledge the importance of active humans and their social relations in society. However, not all of these approaches are critical. The approach, which most clearly can be considered as a critical theory, is Bhaskar’s Dialectical Critical Realism. For Bhaskar, there is a normative feature in dialectical thinking that he terms Moral Realism. Its central feature would be absenting absence. ‘‘This encompasses the absenting of constraints, including ills generally, which comprise lack of freedoms. (y) Dialectic is the process of absenting constraints on absenting absences (ills, constraints, untruths, etc.)’’ (Bhaskar, 1993, pp. 102, 297). Dialectic would be the axiology and pulse of freedom (Bhaskar, 1993, pp. 378, 385). ‘‘Dialectic is the yearning for freedom and the transformative negation of constraints on it’’ (Bhaskar, 1993, p. 378). Bhaskar stresses a quality of critical thinking that is inherent in Marxian thinking: the critique of all domination because it sets limits on human potentials. ‘‘Theory is capable of gripping the masses as soon as it demonstrates ad hominem, and it demonstrates ad hominem as soon as it becomes radical. To be radical is to grasp the root of the matter. But, for man, the root is man himself. (y) The criticism of religion ends with the teaching that man is the highest essence for man — hence, with the categoric imperative to overthrow all relations in which man is a debased, enslaved, abandoned, despicable essence, relations which cannot be better described than by the cry of a Frenchman when it was planned to introduce a tax on dogs: Poor dogs! They want to treat you as human beings!’’ (Marx, 1844, p. 385). Critical theory is materialistic in the sense that it addresses phenomena and problems not in terms of absolute ideas and predetermined societal development, but in terms of resource distribution and social struggles. Reality is seen in terms that address ownership, private property, resource distribution, social struggles, power, resource control, exploitation, and domination. In such an endeavor, a reactualized notion of class is of central importance (cf. Fuchs, 2008a, Chapter 7.3). To make a materialistic analysis also means to conceive society as negativity. To identify antagonisms means to take a look at contradictory tendencies that relate to one and the same phenomenon, create societal problems and require a fundamental systemic change in order to be dissolved. Our critical social systems modeling approach is a non-constructivist one because we ﬁnd it difﬁcult to conceive society as just a construct of the human mind

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(as, e.g., argued by Ernst von Glasersfeld, 2008, cf. also the comment on Glasersfeld’s notion of society by one of the authors of the paper at hand, Fuchs, 2008b). The regularized patterns of society that we encounter and cocreate in everyday life and that seem to enable continuous social activity are evidence that we can be conﬁdent that others exist and are potential partners of communication in an overall shared space that is termed society and that is created by many individuals together and hence is not independent of these individuals, but also not reducible to their cognition, as they require others with whom they mutually create that space. This space is objective in the sense that it is cocreated by humans who in their social relationships create supra-individual regularized patterns of interaction that they can rely on in everyday life and that makes social activity work. Society is not independent of individuals, but also not as radical constructivism seems to claim only subjectively cognitively constructed. Therefore, our notion of self-producing social systems is realistic, it assumes that social reality exists objectively and is recognized and transformed by humans who are parts of social reality and form this reality in interaction with others. Our approach could be classiﬁed as a variety of critical realism (cf. Bhaskar, 1975, 1993, 1998).

3.3

Critical Social Systems Methodology: Unity Through Diversity

Methodology refers to the context of discovery in which scientiﬁc knowledge is conjectured and theoretical assumptions are formulated in relation to empirical ﬁndings. It concerns the approach that is taken to research the ﬁeld in question. Fundamentally, it touches the issue of analysis and synthesis. GST is synthetical, but without denying the role of analyses. In ﬁnding rules of organization and founding modern systems thinking, Bertalanffy ties up to the Aristotelian saying ‘‘The whole is more than the sum of its parts.’’ He does justice to the old Greeks’ concept of cosmos and Aristotle’s holism and teleology as well as to Galilei’s metodo resolutivo. For he argues that analysis is necessary, but nevertheless it does not sufﬁce. ‘‘Unity-through-diversity’’ means in this respect the obligation of not being satisﬁed with the analytical method that yields detailed results of diverse parts but to long for a bigger picture by means of synthesizing these results. Actually, this is the core of systems thinking. It is this approach that makes systems thinking the outstanding method appropriate to coping with complexity. EST takes for granted that in order to be in the position to grasp the bigger picture, it is necessary to admit that there are shortcomings in pure analysis that need to be overcome. Recognizing leaps in quality as, e.g., in system transitions and in-between system levels means acknowledging principles of formalization gaps: they hold it is impossible to ﬁnd an operation in the mind that accomplishes the leap from one quality to another in an unambiguous and compelling way. Those leaps can only be bridged by synthetical thinking. After centuries of predominance of the analytical mode of thinking, the paradigm change has to go in the direction of a synthetic overall view. However, this integrative view of what can be perceived by human intelligence does not need to, indeed must not, be a return to the premodern vision of

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the speculative natural philosophy of antiquity. Rather, it can and must assimilate the knowledge gained from research in every discipline in a historical process which rises from the abstract to the concrete. This way of thinking was applied when Flood and Jackson (1991) dealing with the variety of approaches in the systems movement itself came up with their so-called System of Systems Methodologies, which they called ‘‘complementarism’’ and, after slight modiﬁcation in the tradition of their CST, was, e.g., termed ‘‘discordant pluralism’’ (Gregory, 1996). Complementarism or discordant pluralism does not mean that anything goes. ‘‘There is a need for debate about what are ‘‘good’’ arguments and what are not, and for discussion about how we can choose between different positions that are conﬂicting’’ (Gregory, 1996, p. 54). Though ‘‘different perspectives and systems methodologies should be used in a complementary way to highlight and address different aspects of organizations, their issues and problems’’ (Jackson, 2003, p. 285), they are brought together in a constellation that does not give way to a reduction to a common denominator, but serves as the basis for a discourse that, as Gregory points out, is ‘‘not a relativistic chaos of unrelated factors, but a dialectical model’’ (Jay, 1984, p. 15, cited in Gregory, 1996, p. 54). The question of different perspectives is framed ‘‘in a way that recognizes the legitimacies of each position’’ involved. It ‘‘is a third perspective through which the legitimacies of each value system can be brought together in a critically systemic discourse.’’ This may include that ‘‘such a constellation may legitimately eliminate elements of otherness that have been identiﬁed as illegitimate’’ (Gregory, 1996, p. 55). While the System of Systems Methodologies is conﬁned to the tool box of systems approaches, Mingers made substantial contributions to multi-method research and a pluralist methodology in the realm of sociology and social science and information systems outside systems thinking when, in drawing upon Bhaskar, elaborating a philosophical position called ‘‘critical pluralism’’ (see Mingers, 2001a). He argues for detaching research methods from the paradigm they are espoused with by convention and assign them a role in concrete research tasks independent of the traditional paradigms. But, other than Mingers contends, we believe that putting them into a new context means and shall mean their integration on a meta-level system of methods. Combining both the ideas of a System of Systems Methodologies and Critical Pluralism, i.e., extending the systematization attempt beyond systemic methods to other sociological, social scientiﬁc, and other methods is what is needed, when it comes to critical social systems thinking in our opinion. On a meta-level, a methodology can be built that is a system of methods that, in turn, originate from different theoretical angles, but undergo a process of critical reconsideration in order to suit a common methodological umbrella. The underlying way of thinking is a dialectical account of unity and diversity or identity and difference. Ways of thinking can be seen as ways of considering how to relate identity and difference. The dialectical one — which is opposed to reductionism, projectivism, and disjunctivism — establishes identity in line with the difference; it integrates both sides of the difference (yielding unity) and it differentiates identity (yielding diversity); it is a way of thinking that is based upon integration and differentiation; it is opposed to both uniﬁcation and dissociation and yields unity and diversity in one — unity in

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diversity and diversity in unity. As French social thinker and systems philosopher Edgar Morin puts it: ‘‘It means understanding disjunctive, reductive thought by exercising thought that distinguishes and connects. It does not mean giving up knowledge of the parts for knowledge of the whole, or giving up analysis for synthesis, it means conjugating them. This is the challenge of complexity which ineluctably confronts us as our planetary era advances and evolves’’ (1999, p. 19). Thus, a critical social systems methodology shall be capable of doing justice to methods other than system methods and including them as well. It is critical in that it combines different methods to consider properly the multidimensionality of the world. The concept of re-creation or social autopoiesis takes into account that phenomena don’t have linear causes and effects, but are complex, dynamic, and open to the future. Systems carry certain development potentials in them that at the same time pose positive and negative potentials that are realized or suppressed by human social practice. Dialectical reasoning means acknowledging the existence of contradictions and the search for these contradictions. Dialectical analysis in this context means complex dynamic thinking, realism an analysis of real possibilities and a dialectic of pessimism and optimism. In a dialectical analysis, phenomena are analyzed in terms of the dialectics of agency and structures, discontinuity and continuity, the one and the many, potentiality and actuality, global and local, virtual and real, optimism and pessimism, essence and existence, immanence and transcendence, etc.

4 Conclusion In this paper, we have argued for a turn in systems theory and social autopoiesis theory away from constructivism and functionalism toward critical thinking, dialectics, and human-centeredness. The predominant application of autopoiesis to social systems is Niklas Luhmann’s social systems theory. We identiﬁed the exclusion of humans from social systems as the main problem of Luhmann’s theory. This separation of systems and humans results in an afﬁrmative approach that neglects social problems. Critical social systems theory sees humans at the center of social systems, it argues that humans coproduce and reproduce social structures, which condition further human actions, by which again structures emerge and are reproduced, etc. This dynamic, dialectical process was termed re-creation. Re-creation is an autopoietic process because the unity of human actors and social structures that constitutes sociality is permanently reproduced and reemerging. The acuteness of global societal problems requires that today social theory is not just descriptive and analytical, but also normative and in the interest of oppressed groups and individuals. Therefore, we argued that human-centeredness should also be seen as an important critical feature of contemporary social theory. Are social systems autopoietic? Yes, but we suggest an understanding that is human-centered and therefore departing from Luhmann’s interpretation. We argue that humans permanently create the unity of human actors and social structures, i.e., human sociality, in society. What is permanently created in society is the

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fundamental quality of humans, their sociality. Society reproduces and produces man as a social being, and man reproduces and produces society by socially coordinating human actions. Man is creator of, and created by, society; society and humans produce each other mutually. We try to frame social autopoiesis as a process, in which we ﬁnd a dialectic of social structures and human actors. Luhmann’s focus on communications and structures as unit of autopoietic reproduction is in our approach replaced by the unity of structure and actors. We have argued that this focus allows to build a critical autopoietic theory of organizations and society. The gain of a reinterpretation of autopoiesis that is connected to thinkers like Giddens, Bourdieu, or Bhaskar is a critical focus that we miss in Luhmann’s theory.

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Gregory, W. (1996). Dealing with diversity. In: R. L. Flood & N. R. A. Romm (Eds), Critical systems thinking, current research and practice. New York, NY and London: Plenum Press. Habermas, J., & Luhmann, N. (1971). Theorie der Gesellschaft oder Sozialtechnologie? Frankfurt/Main: Suhrkamp. Hofkirchner, W. (1998). Emergence and the logic of explanation. Acta Polytechnica Scandinavica. Mathematics, Computing and Management in Engineering Series, 91, 23–30. Hofkirchner, W. (2005). Ludwig von Bertalanffy. Forerunner of evolutionary systems theory. In: J. Gu & Chroust, G. (Eds), The New Role of Systems Sciences for a Knowledge-Based Society, Proceedings of the First World Congress of the International Federation for Systems Research. Kobe, Japan, ISBN 4-903092-02-X, 6pp. Hofkirchner, W. (2006). Towards a post-Luhmannian social systems view. International Sociological Association: Proceedings of the XVI World Congress of Sociology: The Quality of Social Existence in a Globalizing World. Durban. CD-ROM. Hofkirchner, W. (2007). A critical social systems view of the Internet. Philosophy of the Social Sciences, 37(4), 471–500. Horkheimer, M. (1937/2002). Traditional and critical theory. In: Critical theory (pp. 188–252). New York: Continuum. Jackson, M. C. (2003). The origins and nature of critical systems thinking. In: G. Midgley (Ed.), Systems thinking volume IV (pp. 77–92). London: Sage. Jackson, M. C. (2003). Systems thinking: Creative holism for managers. Hoboken, NJ: Wiley. Jantsch, E. (1987). Erkenntnistheoretische Aspekte der Selbstorganisation natu¨rlicher Systeme. In: S. J. Schmidt (Ed.), Der Diskurs des Radikalen Konstruktivismus (pp. 159–191). Frankfurt: Suhrkamp. Jay, M. (1984). Adorno. Cambridge, MA: Harvard University Press. Laszlo, E. (1987). Evolution — The grand synthesis. Boston, MA: New Science Library. Luhmann, N. (1984). Soziale Systeme. Frankfurt/Main: Suhrkamp. Luhmann, N. (1988). The autopoiesis of social systems. In: F. Geyer & J. Van der Zouwen (Eds), Sociocybernetic paradoxes (pp. 172–192). London: Sage. Luhmann, N. (1996a). Die Realita¨t der Massenmedien. Opladen: Westdeutscher Verlag. Luhmann, N. (1996b). Protest: Systemtheorie und soziale Bewegungen. Frankfurt/Main: Suhrkamp. Luhmann, N. (2004). O¨kologische Kommunikation. Opladen: Westdeutscher Verlag. Marx, K. (1844). Einleitung zur Kritik der Hegelschen Rechtsphilosophie. In: MEW, Vol. 1 (pp. 378–391). Berlin: Dietz. Maturana, H. (1987). Biologie der Sozialita¨t. In: S. J. Schmidt (Ed.), Der Diskurs des Radikalen Konstruktivismus (pp. 287–302). Frankfurt/Main: Suhrkamp. Maturana, H. R. (1980). Man and society. In: F. Benseler, P. Hejl & W. Kock (Eds), Autopoiesis, communication, and society: The theory of autopoietic systems in the social sciences (pp. 11–32). Frankfurt: Campus. Maturana, H. R., & Varela, F. (1980). Autopoiesis and cognition. Dordrecht: Reidel. Maturana, H. R., & Varela, F. J. (1987). The tree of knowledge. Boston, MA: Shambhala. Mingers, J. (1995). Self-producing systems. Implications and applications of autopoiesis. New York, NY: Plenum. Mingers, J. (1999). Information, meaning, and communication: An autopoietic approach to linking the social and the individual. Cybernetics & Human Knowing, 6(4), 25–41. Mingers, J. (2001a). Combining IS research methods: Towards a pluralist methodology. Information Systems Research, 12(3), 240–259. Prigogine, I. (1980). From being to becoming. San Francisco, CA: Freeman. Reckwitz, A. (1997). Struktur. Zur sozialwissenschaftlichen Analyse von Regeln und Regelma¨Xigkeiten. Opladen: Westdeutscher Verlag.

PART III APPLICATIONS OF AUTOPOIESIS IN ORGANIZATION THEORY

Chapter 7

Productive Misunderstandings between Organization Science and Organization Practice: The Science–Practice Relation from the Perspective of Niklas Luhmann’s Theory of Autopoietic Systems David Seidl

1 Introduction Many recent studies have voiced the growing concern that the body of knowledge that springs from organization science is hardly taken notice of in management practice. This has given rise to urgent calls for making organization research more relevant to practitioners and an intensive debate on how to realize this aim has set in (e.g., Hodgkinson, Herriot, & Anderson, 2001; Rynes, Bartunek, & Daft, 2001; MacLean & MacIntosh, 2002; Baldridge, Floyd, & Markoczy, 2004; Van de Ven & Johnson, 2006). In most of the existing literature one can identify three main reasons for the observable lack of connection between organization research and practice: research is not sufﬁciently focused on the ‘‘real’’ problems of practitioners (e.g., Rynes, McNatt, & Breetz, 1999), research results are not properly disseminated to practitioners (e.g., Spencer, 2001), and the language of science is not properly translated into the language practitioners’ use (e.g., Starkey & Madan, 2001; Van de Ven & Johnson, 2006). The underlying assumption is that if scientists redressed these shortcomings, their ﬁndings would be utilized by practitioners and thus the gap between theory and practice would be bridged. The aim of this chapter is to contrast this recent debate on the relation between science and practice with an analysis from the perspective of Niklas Luhmann’s theory of autopoietic systems. According to this perspective, the lack of any transfer

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of scientiﬁc knowledge to practice needs to be understood as the inevitable result of the differentiation between organization science and the so-called ‘‘management practice,’’ which function according to different logics. This impedes the transfer of knowledge from the ﬁeld of science to that of practice. Hence, from this perspective the practical irrelevance of management science is not a problem that can be resolved. On the contrary, only because of this differentiation, and thus, the impossibility of any direct transfer of meaning, can science be as productive as it is. The idea of organization studies as an ‘‘applied science’’ is a mere illusion. This chapter is structured into six sections. After the present introduction, Section 2 will introduce the basic elements of Luhmann’s theory of social systems as autopoietic systems of communication. Section 3 will describe the conceptualization of science, economy, and organization as three different types of social systems that operate according to different logics of communication. Section 4 will deal with the impossibility of any transfer of meaning between management science and management practice. As we will see in Section 5, this impossibility does not imply that management science and management practice have no impact on each other: science and practice cause in each other mutual ‘‘perturbations,’’ whose meaning is determined by the receiving system. We conclude with some comments on the implications of this perspective for research policy.

2 Social Systems as Autopoietic Systems of Communication Niklas Luhmann (1927–1998) was one of the most inﬂuential sociologists to have drawn on the concept of autopoiesis. The two Chilean cognitive biologists Humberto Maturana and Francisco Varela had introduced the concept of autopoiesis in the early 1970s to conceptualize life, i.e., the aspect that distinguishes what they called a living from a nonliving ‘‘machine’’ (Varela, Maturana, & Uribe, 1974). They write: An autopoietic machine is a machine organized (deﬁned as a unity) as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network. (Maturana & Varela, 1980, p. 78)

Central to the concept of autopoiesis is the idea that a system is produced and reproduced by interactive processes among its components. In other words, through its components the system reproduces itself. In contrast to allopoietic systems, none of the elements of autopoietic systems are produced by agents external to the system. All processes of autopoietic systems are produced by the system itself and all processes of autopoietic systems are processes of self-production. In this sense, one can say that autopoietic systems are operatively closed. There are neither elements entering the system from outside nor vice versa. A system’s operative closure, however, does not imply a closed system model. It only implies that no operations can enter or leave the system. Autopoietic systems are,

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nevertheless, also open systems: all autopoietic systems have contact with their environment (interactional openness). Living cells, e.g., depend on an exchange of energy and matter between themselves and their surroundings, without which they could not exist. The contact with the environment, however, is regulated by the autopoietic system; the system determines when, what and through what channels there is an exchange of energy or matter between itself and the environment (undoubtedly, there are some external forces that might inﬂuence the system directly, e.g., radioactive radiation, which can destroy parts of the system. These inﬂuences, however, can never determine what operations take place in a system). Luhmann (1986, 1995) argued that the concept of autopoiesis, if abstracted from its biological references, could also be applied to other domains, particularly to the social domain. In contrast to other social scientists who used the concept of autopoiesis only metaphorically (e.g., Morgan, 1997) or who tried to apply it directly to the social domain (e.g., Beer, 1980; Robb, 1989; Zeleny & Hufford, 1992), Luhmann ﬁrst abstracted it into a general concept on a transdisciplinary level, and then redeﬁned it as the speciﬁc concept of autopoiesis with reference to particular types of nonbiological systems (Luhmann, 1995; for an overview of different applications of autopoiesis to the social domain; see Mingers, 1995). Apart from living systems Luhmann identiﬁes two additional types of autopoietic systems: social systems and psychic systems (or minds). While living systems reproduce themselves via biological processes, social systems reproduce themselves via communication processes, and psychic systems via mental processes. Whereas the elements of living systems are physical substances, those of social and psychic systems are elements of meaning. In the following we will concentrate on Luhmann’s theory of social systems. For Luhmann the elements of social systems are communications (Luhmann, 1986, 1995). Yet, in contrast to the conventional notion of communication as the transfer of meaning from a sender to a receiver, Luhmann conceptualizes it as the unity of three components: (1) information, (2) utterance, and (3) understanding. ‘‘Information’’ refers to the question of what is being communicated while ‘‘utterance’’ concerns the question of how and why it is communicated. Yet, the central component of communication is ‘‘understanding,’’ which is absent from most other conceptualizations of communication. Understanding is the distinction between information and utterance. For a communication to be understood, the information has to be distinguished from the utterance: what is being communicated must be distinguished from how and why it is communicated. It is the understanding which determines the other two components, i.e., the information and the utterance. In this context, Luhmann (1995, p. 143) writes: ‘‘Communication is made possible, so to speak, from behind, contrary to the temporal course of the process.’’ Luhmann argues that communication conceptualized as the unity of utterance, information, and understanding cannot be produced by a human being alone; a single individual might produce an utterance containing a particular piece of information, but he or she cannot contribute the element of understanding as well. This means that it always takes at least two individuals to co-produce this unity. Consequently, communication is conceptualized as an emergent phenomenon that arises from the contact between different individuals.

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Luhmann goes on to explain that how a communication is understood can only be determined from communications that follow on as a reaction to the initial one, in the same way that the concrete meaning of a word in a text is only deﬁned through the words following it in the text. Or, more to the point, the meaning of a communication is the difference that it makes in following communications. However, the difference that a communication makes for ensuing communications is not determined by the focal communication itself but by the other communications. In this sense, Luhmann speaks of communications (i.e., the meaning realized by the communications) as the product of (other) communications and not of individuals (Luhmann, 2002, p. 169). On the basis of this insight, Luhmann unfolds his theory of autopoietic communication systems: every communication belongs to a particular communication system (i.e., a network of communications) by which it has been produced and in whose reproduction it takes part. Examples of such communication systems are organizations or face-to-face interactions. Such communication systems are operatively closed. By this he means that communications are only produced by the particular networks of communications; they cannot be imported from outside those networks. Communication processes are stimulated or triggered from outside (in this sense the system can be said to be interactionally open). For example, a thought in the mind of an individual might stimulate a communication, but it is the network of communication itself that, in reaction to it, produces — according to its own logic of reproduction — a particular communication. As Luhmann writes: The mind cannot instruct communication, because communication constructs itself. But the mind is a constant source of impulses for the one or the other turn of the operative process inherent in communication. (Luhmann, 2002, pp. 176–177)

In other words, communication systems do react to external impulses but they react to them according to their own internal logic; external impulses might trigger certain communicative processes but they cannot determine from outside what internal processes are triggered. In this sense, external impulses constitute merely unspeciﬁc ‘‘perturbations’’ (Luhmann, 1995). Luhmann distinguishes between three types of different social systems: organization, face-to-face interaction, and society. Each of these types of systems reproduces itself on the basis of a different type of communication. Organization is a social system that reproduces itself on the basis of decision communications (Luhmann, 2003, 2005b). Face-to-face interactions are systems that reproduce themselves on the basis of communications that reﬂect the physical presence of the participants. Finally, society is the all-encompassing social system that comprises all interconnected communications. According to Luhmann we have only one world society (except for a handful of tiny societies that live in complete isolation from the global population, such as certain bush tribes) as all communications in the world are interconnected in some way. What is typical of the modern society is its differentiation into several functional (sub)systems, i.e., systems that fulﬁll particular societal functions: e.g., the economic system, the system of science, the legal system, the political system, the system of religion, and the system of education. Each of these subsystems reproduces itself on

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the basis of a particularly coded communication; that is to say, each communication carries a particular code, which identiﬁes that communication as belonging to a particular function system and determines what kind of meaning is being processed (Luhmann, 1989). For example, the economic system processes communications that carry the code ‘‘revenue/expense,’’ while the legal system carries the code ‘‘legality/ illegality.’’ In this sense, the communications of these two systems only process meanings about revenue/expense and legality/illegality, respectively. Other functional systems carry other codes, and thus process different meanings. In the following section we will elaborate on this further.

3 Science, Economy, and Organization as Three Different Types of Systems In order to examine the relation between management science and management practice it is necessary to identify the relevant social systems and to analyze their different logics of communication. Management science belongs to the functional system of science. Management practice, however, cannot be so clearly allocated to any particular functional system; ‘‘practice’’ is not a system in the way that science is. What is usually referred to as ‘‘management practice’’ corresponds at least to two kinds of systems: the economic system and the system of organization. In the following we will analyze the mode of operation of these systems in more detail. As described above, the system of science and the economic system are functional subsystems of society. These subsystems are themselves operatively closed with regard to each other in the sense that each reproduces itself on the basis of a particularly coded communication. Communications within the system of science carry the code true/false (Luhmann, 1989, 1990). That is to say, in order to be considered part of a ‘‘scientiﬁc’’ discourse, a communication has to refer to earlier scientiﬁc communications as either true or false; and it must also be possible for ensuing communications to refer to this communication as either true or false. In this sense, the meaning of a communication within a scientiﬁc discourse is basically its truth or falsity, to which further communications can refer in order to afﬁrm their veracity (which may then be rejected by yet further communications). One of the clearest examples of this is the tendency of scientiﬁc publications to reference other scientiﬁc publications in order to claim their own truth value (cf. Kieser, 2002, p. 208). Kieser explains: Operations of science always refer to other operations within this system. For example, a scientiﬁc publication always refers to other scientiﬁc publications – to theoretical concepts and methods it builds on and develops further. (Kieser & Wellstein, 2008, p. 507)

Drawing on Luhmann, Nicolai writes: Science is from this perspective a web of communications that reproduces, on the basis of scientiﬁc communication, further scientiﬁc communication in turn. (Nicolai, 2004, p. 956)

Whether a scientiﬁc communication is classiﬁed as ‘‘true’’ or ‘‘false’’ is itself entirely determined by the criteria of the scientiﬁc discourse; a ‘‘true’’ scientiﬁc

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communication, in this sense, is a communication that has been accepted by the other scientiﬁc communications as ‘‘true’’ — it is a ‘‘coded truth.’’ The scientiﬁc system is operatively closed in that scientiﬁc communication is only produced by the network of other scientiﬁc communications. Scientiﬁc communication cannot draw on nonscientiﬁc communications in order to substantiate any scientiﬁc claims. For example, a communication that was substantiated with reference to a newspaper article would be considered unscientiﬁc and thus not be incorporated in the network of scientiﬁc communications. The processing of scientiﬁc communications is guided by theories and methodologies that constitute the structures (or ‘‘program’’) of the scientiﬁc system. Theories and methodologies deﬁne the ‘‘rules’’ of what constitutes an acceptable scientiﬁc communication; i.e., they deﬁne how scientiﬁc communications can be related to other scientiﬁc communications, and thus, ultimately, whether or not a particular communication is treated as true or false, or whether it should be ignored as unscientiﬁc. They also determine how to construct scientiﬁc communications from empirical observations. Different theories and methodologies will lead to different scientiﬁc communications. In line with the concept of autopoiesis, the theories and methodologies are not introduced from outside but are themselves the product of scientiﬁc communications. New theories and methodologies are developed on the basis of existing theories and methodologies. Whether or not new theories and methodologies are considered true or false, and thus whether one can substantiate further scientiﬁc communications, depends entirely on the network of scientiﬁc communications (Luhmann, 1990). As a consequence of this, scientiﬁc discourses are necessarily highly ‘‘stylized’’ (Astley & Zammuto, 1992); they construct abstract variables that are meaningful only in a scientiﬁc context as they have mostly very little to do with how the ‘‘same’’ phenomena are treated elsewhere. An example of this is the way in which the concepts of ‘‘performance’’ and ‘‘success factors’’ are constructed in the management sciences (March & Sutton, 1997). In addition to the construction of idiosyncratic variables, the scientiﬁc discourse forces a communication also into relating its variables to each other in an idiosyncratic manner. Thus, within the scientiﬁc discourse, a phenomenon is structured differently from the way in which it would be structured within any other discourse. To give an example, within the scientiﬁc discourse, one assumes explicitly counter-factual situations and works with ceteris paribus clauses. As Luhmann writes: The assumption of ceteris paribus is the condition of isolating the objects of research, but like the presuppositions of model-formation it is a consciously false assumption. Only through false assumptions can true knowledge be attained. (Luhmann, 1989, p. 81)

That is to say, science structures phenomena in such a way as to render them subject to the scientiﬁc criteria that determine truth and falsity. In contrast to the science system, the economic system is not guided by the code true/false but by that of revenue/expense — or simply payment/nonpayment (1988Luhmann, 1989). More speciﬁcally, as Luhmann explains: By the economy we mean all those operations transacted through the payment of money. Whenever money is involved, directly or indirectly, the economy is involved regardless of who makes the payment and whose needs are affected. (Luhmann, 1989, p. 51)

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An economic communication is, e.g., the placing of an order. In this case the meaning of the communication for further economic communications is not its truth or falsity but the payment associated with it. Other economic communications connect to this communication with regard to its effects on payment. The communication may be rejected by ensuing communications, if it is considered to lead to increasing expense (i.e., to be unproﬁtable), or, conversely, it may be accepted, if considered to lead to increasing revenues (i.e., to be proﬁtable). Again, what qualiﬁes as proﬁtable/unproﬁtable is determined entirely by the economic discourse itself. As in the case of the scientiﬁc system, the processing of economic communications is guided by speciﬁc structures. The structures of the economic system are budgets and balances. They deﬁne the rules for economic communication and determine what money can be spent for what purposes. Again, these structures are not introduced from outside but are themselves the product of the economic communications. The third type of system that needs to be examined is the organization. Unlike the other two systems (science and economy), organizations belong to a type that is very different from that of functional subsystems of society. As described above, organizations are systems that reproduce themselves on the basis of decision communications. To appreciate this it is necessary to clarify Luhmann’s concept of decision (Luhmann, 2000, 2005b). In contrast to other conceptualizations of decisionmaking in the literature, for Luhmann decisions are decision communications; it is not that decisions are ﬁrst made and then communicated. Decisions are a very peculiar form of communication: they are ‘‘compact communications’’ (Luhmann, 2000, p. 185) which communicate their own contingency. In contrast to an ‘‘ordinary’’ communication, which only communicates a speciﬁc content that has been selected (e.g., ‘‘I love you’’), a decision communicates also — explicitly or implicitly — that there are alternatives that could have been selected instead (e.g., ‘‘We are buying machine A and not machine B’’). They communicate not only what has been decided but also that it has been decided. This has signiﬁcant implications for the dynamics of decisions. In the transition from one decision to the next, the uncertainty of the ﬁrst decision situation — i.e., the uncertainty about the consequences of the given alternatives — disappears. For the second decision it is irrelevant what the initial decision situation looked like. The second decision can take the chosen alternative as a clear point of reference without having to evaluate the ﬁrst decision situation; i.e., the ﬁrst decision has been ‘‘decided’’ and does not have to be ‘‘decided’’ once more. As such, every decision makes possible extremely complex decision processes by producing stable points of reference for ensuing decisions. As in the case of the other two systems described above, the processing of decisions is guided by particular structures, which Luhmann refers to as ‘‘decision premises.’’ These decision premises deﬁne what decisions come about. There are different types of decision premises. Decision programs or ‘‘plans’’ are such an example: a strategic plan deﬁnes, e.g., a general direction for future decision-making. These decision premises are, again, not introduced from outside but are the product of the organization’s decision processes. Decision premises result themselves from decision

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processes; e.g., strategic plans are the outcome of decision-making processes, which themselves are guided by other decision premises, such as those concerning decisionmaking competences (Luhmann, 2005b). The three systems that we have described (science, economy, and organization) are very different in the way they process meaning, as has hopefully become clear by now. In the following section we will examine what consequences this has for the possibilities of transferring meaning from science to ‘‘practice.’’

4 The Impossibility of Transfer of Meaning between Management Science and Management Practice In the literature it is usually assumed that scientiﬁc knowledge can be transferred to the domain of practice — at least in principle. From the perspective of social-systems theory this is, however, highly questionable. As we have seen in the last section, the three systems potentially involved in such a ‘‘transfer’’ operate according to different logics because of which their respective communications become incommensurate. The differences between the different communication systems become even clearer when we analyze the way they process information (Luhmann, 1989; Seidl & Becker, 2006). Information can generally be deﬁned as ‘‘a difference which makes a difference’’ (Bateson, 1972, p. 315). For each of the three types of systems there is a different type of ‘‘difference that can make a difference.’’ For the science system it is the difference between truth and falsity; only communications that distinguish between true/false have an information value for further scientiﬁc communications. Analogously to the way that computers can only distinguish between 0 and 1, scientiﬁc discourses only distinguish between true and false. Other distinctions have no information value — they make no difference; they are simply ‘‘noise.’’ Similarly, the economic system can only process information in the form of payment/ nonpayment. ‘‘True/false’’ as such is not a difference that makes a difference to the economic system. Organizations also process meaning in the form of decisions. Decisions can only be substantiated by other decisions (of the same organization) and might refer to some external sources. Ultimately, however, which external sources are drawn upon, and in what way, has to be justiﬁed by reference to decisions. The only difference that makes a difference to an organization is the absorption of uncertainty. Whether or not something is true or false, or whether it constitutes a revenue or expense has no information value as such. What is relevant here is whether it provides a source of uncertainty absorption. Because of their different forms of self-referential information processing, the communications of different systems — here: science, economy, and organizations — have different meanings in each system and cannot be translated into each other. Luhmann (2005a) writes that, in order to transfer a scientiﬁc communication into a different social system, it would be necessary to transfer also the entire background of theories on which the particular communication is based — and the theories on which these theories are based in their turn. In other words, it would be necessary to

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transfer more or less the entire scientiﬁc system into the other system. But even if this was possible, the meaning of the communication in another system would necessarily differ from its meaning in the original system, as the entire complex would be interpreted according to a different code. Drawing on Luhmann’s systems theory, Kieser and Nicolai (2004) described how the system of science constructs the problems that it analyzes in a self-referential way that has very little to do with the problems faced by practitioners. This is inevitable as the problems are, by deﬁnition, framed differently in the domains of science and ‘‘practice,’’ even if scientists and practitioners cooperate. Kieser and Nicolai write: [T]he negotiation of a problem deﬁnition [y] has to be seen as a communicative process that depends on agreeing on a speciﬁc frame of reference. In the case of science, frames of reference are derived from extant theories. In the case of performance studies, sometimes they are triggered by a problem that plagues practitioners – for example, by the question of whether the existence of formal procedures of strategic planning are correlated with organizational performance. Soon, however, the discussion between researchers via their publications creates new and different problems, and the problem that initiated the scientiﬁc discourse gets lost from sight. (2004, p. 276)

Even when it is the same individuals who ‘‘participate’’ in the scientiﬁc and ‘‘practical’’ discourses, they cannot transfer meaning from one discourse to another; ‘‘their’’ communications and actions are determined rather by the logic of the particular communication systems (Luhmann, 1986). Nicolai (2004) demonstrated this impressively in his study of Porter’s work, which is widely considered a prime example of applied research. Rather than crossing the boundaries of the scientiﬁc and the ‘‘practical’’ discourses, the economics-based scientiﬁc parts and the ‘‘applied’’ parts of Porter’s work are presented more or less autonomously from each other. Kieser and Nikolai conclude: In short, in discourses in which researchers try to establish the validity of theories on the basis of scientiﬁc criteria, science necessarily disconnects itself from discourses in which practitioners evaluate the usefulness of a concept. Again, the practice-oriented researcher ﬁnds himself or herself thrown back on that self-referential stream of communication that is typical for scientiﬁc discourses and is perceived as detached from the real world. (2004, p. 277)

5 Perturbations between Science and Practice As we have argued in the last section, a direct transfer of scientiﬁc results into practice is not possible due to the different logics of communication. However, this does not mean that management science is irrelevant to management practice. Both the systems of economy, in general, and business organizations, in particular, are inﬂuenced by management science. In this sense one can speak of the ‘‘practical relevance’’ of science whenever science makes a difference to practice — even though it does not make the same difference as it makes for science itself. Hence, scientiﬁc knowledge can be said to be of relevance to business organizations if it has some relevance to decision-making, or more to the point, if it makes a difference to

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decision-making. This is in line with Luhmann’s conceptualization of practical relevance: [W]e should analyze the application of scientiﬁc results in practice not in terms of action but in terms of making decisions. It is not a question of whether something, which from a scientiﬁc point of view has been acknowledged as correct action, is reproduced correctly or not; rather the question is whether the decision situation is modiﬁed through the incorporation of a scientiﬁc result, which may (but doesn’t have to) affect the ultimately selected alternative. (Luhmann, 1993, p. 330; my translation; emphasis added)

The particular form of relevance for decision-making can vary. For example, scientiﬁc knowledge might contribute to deﬁning the decision situation, deciding between alternatives or enforcing a decision. Thus, whether a scientiﬁc theory is true or not is irrelevant to ‘‘practice’’; the question is whether the referral to a particular theory helps achieve certain aims. Luhmann illustrates this with an example from psychology: With regard to the question of applicability it is irrelevant whether the Oedipus complex really exists; what counts is whether somebody who is skilled in identifying it is able to combine situations and therapies in a successful way. (Luhmann, 1993, p. 323; my translation)

In contrast to conventional conceptions of the application of scientiﬁc knowledge in practice, according to which elements of meaning are simply infused into the receiving system, from the systems perspective the ‘‘application’’ of such knowledge is associated with a change of meaning. Once knowledge is ‘‘applied,’’ the meaning is not the same; it is not even translated. It is simply a different meaning that is received in practice. Luhmann (1993, p. 327) refers to this as a ‘‘productive misunderstanding.’’ Teubner explains: In a precise sense, interdiscursive translation is impossible. Here lies the paradox of today’s babylonic language confusion. Between the discourses, the continuation of meaning is impossible and at the same time necessary. The way out of this paradox is misunderstanding. One discourse cannot but reconstruct the meaning of the other in its own terms and context and at the same time can make use of the meaning material of the other discourse as an external provocation to create internally something new. (Teubner, 2000, p. 408)

As Teubner explains, one system cannot receive input of meaning from another system; it merely reconstructs elements of another system according to its own logic. This internal reconstruction is, however, its very own construct, which is different from the original one. Luhmann writes: Non-identical reproduction thus means: a change of meaning through re-contextualization, through integration into a new neighborhood, through triggering of different associations. Whether the infused element was true or false quickly loses its relevance. (Luhmann, 1993, p. 330; my translation)

From this perspective, the introduction of scientiﬁc knowledge into an organization causes within the discourse an unspeciﬁc perturbation (Teubner, 2000), which elicits reactions from the system that are speciﬁc to that system. Drawing on Luhmann’s systems theory, Seidl (2007) argues that what is often described as the introduction of a new concept into an organization is actually just the introduction of

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a label, the meaning of which is then constructed according to particular structures of the organization. In other words, the organization constructs its own meaning. Empirically, one ﬁnds that whenever organizations proclaim that they are applying new concepts — either from science or from other external systems — there are usually long discussions about how the labels associated with those concepts can be interpreted and related to existing practices (cf. Zbaracki, 1998). Organizations try to make sense of the new labels on the basis of their existing discursive structures and in this way create new sense, i.e., new meaning. Given that the economic system in general and business organizations in particular have such different modes of information processing, their reaction to scientiﬁc communications is a phenomenon in need of explanation. Rather than being surprised at the fact that scientiﬁc results have such limited effects on other systems, one should be surprised that they affect them at all. An explanation for this is provided by the concept of ‘‘structural coupling’’ (Maturana, 1978; Luhmann, 1995). Systems are said to be ‘‘structurally coupled’’ if their respective structures are adjusted to each other in such a way as to allow systematically for mutual perturbations. Thus, structural coupling can explain why systems, despite their operative closure (i.e., in spite of the impossibility to exchange elements), remain responsive toward other systems in their environment. The most general form of structural coupling between social systems is language. All social systems are structured in such a way as to be able to process language (Luhmann, 1995). However, every social system does so in a different way. What’s more, there is a particularly close structural coupling between certain areas of the sciences and other systems where they share a particular language. For example, in all strategy discourses — whether in the scientiﬁc system or business organizations — one ﬁnds that more or less the same strategy language is used. Every strategy discourse can make (its own) sense of the labels ‘‘strategic planning,’’ ‘‘strategy review,’’ ‘‘strategic forecasting,’’ etc. — something that might have no meaning at all in other types of discourses. Because of that, different strategy discourses have particularly strong ‘‘resonance’’ (Luhmann, 1989) with regard to each other — where ‘‘resonance’’ means that a system reacts to external events in accordance with its own logic (Luhmann, 1989, p. 145). The degree of resonance with regard to scientiﬁc communications in a particular organization varies with the degree to which the organization’s structures are ‘‘aligned’’ with the structures of the scientiﬁc system. While some organizations take almost no notice of scientiﬁc results, other organizations adjust their structures explicitly to the structures of the science system. A good example of this are consulting ﬁrms, which try to stay in close ‘‘contact’’ with the developments in management science. In other words, they couple themselves structurally to the system of science. Because of that, new developments in management science have a particular resonance in many consulting ﬁrms; i.e., such developments can be seen as a difference that makes a difference to consulting ﬁrms, even though — this needs to be stressed again — each consulting ﬁrm determines by itself what difference this makes. Often this might amount to no more than using a particular scientiﬁc label to enforce a particular decision (Kieser, 2002).

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Apart from coupling themselves structurally to the system of science, consulting ﬁrms often also function as means of structural coupling between science and other organizations. Thus, not only do the consulting ﬁrms possess resonance with particular developments in science, but they can also serve as a means of creating resonance within other organizations. This is the case if organizations couple themselves structurally — as clients — to consulting ﬁrms (on the consultant–client relation see Luhmann, 2005a; Mohe & Seidl, 2007) that are themselves coupled to the scientiﬁc system. In this way, certain scientiﬁc ﬁndings, e.g., the identiﬁcation of a new ‘‘success factor’’ in science, might have some resonance in the consulting ﬁrm in the sense that the ﬁrm changes its concepts of consulting, which might have an effect on what perturbation these concepts cause in the client. Such effects might take different forms, and consist in something as simple as, e.g., the adoption of new scientiﬁc labels that are (re)constructed by the consultants and clients. What meaning is ultimately created within a particular organization in reaction to the introduction of a new label is not completely random. Rather, the particular communicative context, i.e., the particular structures of communication into which the new labels become embedded, restrict the range of possible meanings that may be attached to the labels. This is particularly the case where there is a whole set of labels to accommodate. In these cases the interpretation of every individual label has to ﬁt with the interpretation of the other labels — unless, i.e., one only selectively draws on individual labels (cf., Zbaracki, 1998). It is impossible to determine from the outset what meaning will ultimately be created in response to a perturbation from the scientiﬁc system. As Teubner writes: There is of course, no built-in guarantee that such a misunderstanding will be productive. You cannot say in advance whether in the famous shell, the irritation of the [grain of sand] will at the end create the pearl. (Teubner, 2000, p. 409)

In some cases organizations might accomplish a fundamental change. In other cases the ongoing practical discourses might hardly be affected: the organization might use the new labels but without really changing its structures of communication. There are many empirical accounts in the management literature of such instances of pure relabeling (e.g., Ashforth & Gibbs, 1990; Brunsson & Olsen, 1993). While this is often portrayed as intentional deception, there are many cases where it is assumed by the organization itself that the organizational practices have been changed: through the new labels, the organizational reality is experienced differently, even if it has not changed in any ‘‘substantial’’ way. The system-speciﬁc reinvention of a scientiﬁc concept is, however, only possible if the organization ﬁnds some point of connection between its existing interpretational context and the label. That is, the different labels and sublabels in some way or other must be open to being interpreted according to the existing interpretational context: the labels need ‘‘interpretive viability’’ (Benders & Van Veen, 2001), i.e., they have to leave scope for interpretation. Several authors have commented on the ambiguity and vagueness of scientiﬁc concepts or labels that are used in practice (e.g., Kieser, 2002; Ortmann & Salzmann, 2002; Nicolai, 2004). While some authors see this ambiguity fairly critically, other authors have pointed out — in accordance with our

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systems-theoretical perspective — that only by being ambiguous and vague is there a chance of such concepts/labels being able to be made to ‘‘ﬁt’’ the concrete organizational context. Astley and Zammuto write in this respect: Linguistic ambiguity [y] gives conceptual terminology great ﬂexibility of application, allowing words to take on new meanings in the context of a different language game. (Astley & Zammuto, 1992, p. 453; emphasis added)

Ambiguity allows the organization to project the decision problems it encounters into a concept and thus interpret it as the solution to these pressing problems (Kieser, 1997, p. 59; Kieser & Wellstein, 2008). While, in principle, any scientiﬁc communication can have practical implications if the economic system or the business organizations react to them, the likelihood of causing such resonance can also be inﬂuenced by the scientiﬁc system. In line with this, Luhmann (1993) suggests conceptualizing ‘‘applied science’’ as a science that contemplates, and tries to increase its potential for causing perturbations in practice. This contrasts with the widely held concept of ‘‘applied science’’ as a science whose ﬁndings are transferred into practice — which is impossible in principle. In other words, the scientiﬁc discourse might reﬂect on its potential for stimulating parallel processes in the practical discourse; for stimulating, that is, productive misunderstandings. This reﬂection can focus on the aspect of content or process. With respect to content, science might, e.g., try to develop labels that are likely to have resonance also in the practical discourse. With respect to process, one might try to ensure that the process of research comes into ‘‘contact’’ with management practice. This might take, for instance, the form of the so-called ‘‘mode 2 management research’’ (e.g., Gibbons et al., 1994). However, in contrast to the usual interpretation, ‘‘mode 2’’ would have to be understood as a parallel processing of separate discourses: the scientiﬁc discourse and the practical discourse take place at the same time, with the ‘‘same’’ communications meaning different things in the different discourses.

6 Conclusion: Implications for Research Policy In this chapter we tried to demonstrate the potential of Luhmann’s theory of autopoietic social systems for illuminating the relation between management science and management practice. For this purpose we ﬁrst introduced Luhmann’s concept of social systems as operatively closed systems of communication. According to this view, science constitutes a particular system that is characterized by a particular logic of communication that differs fundamentally from that of the systems of ‘‘practice’’ — in this case: the economic system in general and business organizations in particular. Due to their different logics, scientiﬁc results cannot be transferred to the other systems. However, this doesn’t mean that science has no impact at all on practice: science can cause ‘‘perturbations’’ in other systems, which might change those systems’ structures. We argued that whether science can have such an effect depends on the structures of the relevant systems.

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With regard to the current debate on the practical irrelevance of management science (e.g., Rynes et al., 2001; Baldridge et al., 2004; Van de Ven & Johnson, 2006), this has important implications: from this systems-theoretical perspective, the lack of practical relevance needs to be understood not as a deﬁciency of the particular research but as an inevitable consequence of the incommensurability between the different discourses. In a strict sense, we might thus only speak of the relevance or irrelevance of research to further research — but not to practice. The implication of this is twofold: on the one hand, management research has to acknowledge its selfreferentiality as constitutive. That is to say, management science — like all science — only progresses by focusing on the scientiﬁc discourse (cf., Kieser, 2002; Nicolai, 2004). Thus, the ‘‘difference’’ or ‘‘gap’’ between management science and management practice, which has been deplored by so many management scholars as a problem that needs to be done away with, has to be appreciated as the sine qua non without which management science cannot constitute a science at all. If management science were adjusted to the logic of management practice, it would no longer constitute a science but simply be another form of management practice (Luhmann, 1994; Kieser, 2002; Kieser & Leiner, 2009). On the other hand, the scientiﬁc discourse could take into account that it has the potential to have resonance in the practical management discourses and thus constitute a fruitful source of perturbation (Astley & Zammuto, 1992; Luhmann, 1994; Seidl, 2007). As such, management science could try to increase its potential for having some resonance in management practice. However, there are two dangers: ﬁrst, due to the different logics of the science system and the receiving systems, the former has no control over its effect on the latter; the perturbation might prove detrimental. In this respect, science one can only try to draw the attention of the receiving system to the fact that it is the receiving system itself that determines the effect of the scientiﬁc perturbation. Thus, science cannot be blamed for any detrimental effects that its concepts might have in practice. Second, if management research focuses too much on its effect on practice, it might lose its connectivity to other scientiﬁc communications; i.e., it might become disconnected from the network of scientiﬁc communications and thus no longer constitute an element of the scientiﬁc system (Luhmann, 1994; Kieser, 2002). In this respect, science might reﬂect on its own orientation with regard to internal and external ‘‘audiences’’ and try to strike some sort of balance. There are numerous examples of such reﬂections taking place in management science — this paper is an example of that.

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Chapter 8

Plugging the Theoretical Gaps: How Autopoietic Theory Can Contribute to Process-Based Organizational Research John Brocklesby

1 Introduction In examining what role autopoietic theory might play in furthering the agenda of process-based organizational research, it is worth noting that the biological notion of autopoiesis and derivative concepts have already achieved limited recognition in the broad organization studies ﬁeld. A perennial debate has evolved around the question of whether organizations can and/or should be considered autopoietic (see Luhmann, 1986; Zeleny & Hufford, 1992; Mingers, 1992; Robb, 1989; Kay, 2001). Beyond that, the general approach seems to involve taking some deﬁned aspect of autopoiesis and employing this to shed light on some deﬁned aspect of organizational life. Thus, Krogh and Roos (1998) use the concept of autopoiesis to expound, discuss, and illustrate a distinctive perspective on organizational knowledge; Luhmann (1990) and Teubner (1984) use autopoiesis to create awareness of how the circularity and selfreferentiality of legal, and social systems more generally, can prevent renewal and lead to a failure in adapting to problems in society. Autopoiesis has been used to enhance our understanding of how the functioning of computers relate to the evolution of human language, thought and action, (Winograd & Flores, 1987). In management, the concept of autopoiesis has been used, largely in a metaphorical sense, to understand the ﬁrm as a living evolving system that is characterized by ‘‘ﬂux and transformation’’ (Morgan, 1986). In the therapeutic professions, various writers use autopoiesis to show how circular sets of self-reinforcing conversations can create severe dysfunctions with individuals (Efren, Lukens, & Lukens, 1990), in families and in other tightly knit social groups (Dell, 1982, 1985; Hoffman, 1988; Goolishian & Winderman, 1988). Elsewhere in organization studies, Kay (1997) applies autopoiesis

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to the facilitation of organizational change, and Beer (1981) uses the term ‘‘pathological autopoiesis’’ in understanding threats to organizational viability. Applications of the autopoiesis concept such as these raise important new questions about organizations and the behavior that takes in and around them. Despite this, there remains a sense that more could be done to exploit the full potential of this body of knowledge, that it could, and perhaps should have a higher proﬁle than it currently does. More ambitiously perhaps, one might even entertain the thought that autopoietic theory might provide a coherent paradigm for theorizing and studying organizational phenomena more generally. Against this background, the main purpose of the chapter is to sketch out what might potentially be a much more signiﬁcant role for autopoietic theory. The argument revolves around aligning it with a major trend that has been underway within organization theory for some time, a trend that appears to be becoming increasingly inﬂuential. I am referring here to the shift toward process thinking and the corresponding ‘‘turn to language’’ that is a key aspect of it. In what follows, the chapter begins by outlining the broad characteristics of process thinking in organizations, and — within this frame — what is meant by the ‘‘turn to language.’’ It then considers two process and language-based approaches which have been highly inﬂuential in organization studies. These are Foucauldian discourse analysis and social constructionism. The chapter then turns to autopoietic theory with a view to showing how it can build upon and extend these approaches and provide answers to questions that remain theoretically underdeveloped in these other approaches.

2 Process Thinking and the Recent ‘‘Turn to Language’’ in Organization Studies If the number of papers, edited works, symposia, workshops, and conferences devoted to this issue is any guide (see, e.g., Gergen, 1992; Hosking & McNamee, 2002; Townley, 1993; McKinlay & Starkey, 1998; Burr, 1995; Knights & Morgan, 1995; Shotter, 1995; Wood et al., 2006), there has been a major renewal of interest in organizational process thinking and, within this, in the speciﬁc role played by language. With process thinking, it is clear that this has a signiﬁcantly different meaning today than has been the case previously. In the past — often within a broad ‘‘systems’’ perspective — this has generally been taken to be about the various ﬁnancial, material, human, and informational ﬂows that take place within the boundaries of the ﬁrm that sustain the transformation of a deﬁned set of environmental inputs into outputs. All of this is done in support of some purpose. On this account, the organization, its boundaries, and the phenomena that occur within and beyond these are taken as preexisting givens. There is a strong emphasis on explaining such phenomenon in terms of cause–effect logic with a view to maximizing control in support of taken-for-granted organizational goals. Classical management theory, the functionalist perspective in organization theory, and the so-called ‘‘hard’’ systems approach all reﬂect these sorts of concerns.

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Nowadays the meaning of process thinking has changed substantially; the emphasis is also more on understanding organizations than it is on controlling and managing them. Speciﬁcally, process thinking deals with questions of ‘‘emergence’’ and ‘‘becoming.’’ Here, actions and ‘‘doings’’ are privileged over entities, and process is privileged over structure. In the mainstream of organization studies, propositions tend to be formulated in terms of entities and ‘‘things’’ that are presumed to exist independently of people. Examples might include ‘‘the environment,’’ ‘‘the organization,’’ ‘‘the strategy,’’ ‘‘the opportunities and threats,’’ ‘‘the structure,’’ ‘‘the leadership,’’ ‘‘the culture,’’ and so on. In process thinking propositions are more contingent and are couched in terms of the dynamics of social interaction. A few examples will illustrate this basic feature of process thinking. Eden (1992) speaks of corporate strategy as the outcome of a social process that is relatively independent of what those involved might consider to exist in the internal and external strategic environments; Barrett, Thomas, and Hosevar (1995) reconceptualize the organizational change process from a rational planning perspective to one that emphasizes the role of discourse in determining fundamental assumptions that govern corporate transformation; Barry and Elmes (1997) examine the role of ﬁction in managers’ attempts to make strategic discourse credible; Blackler, Crump, and McDonald (2000) describe how micro social networks develop inﬂuential systems of organizational knowledge; Chia and Holt (2006) and Chia and MacKay (2007) show how strategic activity in organizations derives more from non-deliberate everyday practical ‘‘coping’’ activity and habituated dispositions than from deﬁned actors engaging in deliberate, purposeful, goal-setting initiatives that relate to a ‘‘knowable’’ environment. Finally, Cunliffe (2001) explores how managers attempt to construct a sense of who they are, create a shared sense of features of their organizational landscape, and how they move others to talk and act in different ways through their dialogical practices. Propositions such as these are all premised on the basic idea that it is not an independent world ‘‘out there’’ that determines the ‘‘objects’’ that are the basis of inquiry and/or organizational action. Instead ‘‘objects’’ are constituted by and the outcome of social processes. Although organizational research is still interested in propositions about objects, it is more interested in the processes that create and sustain things ‘‘in the making.’’ This perspective invokes a fundamental shift in epistemology. Once we take ‘‘objects’’ to be things that we create through social processes, any reference to an independently existing world that can be objectively described and explained becomes redundant. In summary, process thinking is interested in how tangible and abstract organizational phenomena evolve, how they develop, how they are sustained, and how they alter over time. It is about the relationship between the production and reproduction of organizational processes and the local subjective worlds of the people involved. Process thinking portrays this as an ongoing journey; one that is conditioned by the past, but always constituted in the present. Within this framework, language assumes particular importance. If one accepts the basic premise that the world is not simply something that exists ‘‘out there,’’ then one has to identify mechanisms through which the various worlds that people experience

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are ‘‘brought forth.’’ Language provides such a mechanism; it provides a means through which people’s organizational realities are constituted, and through which the dynamic landscape of organizational life is continually enacted, negotiated, and legitimized. Virtually all process-based organizational research acknowledges the social basis of language. Whether we are speaking of rudimentary linguistics distinctions, vocabulary, concepts, metaphors, conversations, discourse, narratives, rhetoric, stories, or all of these, there is recognition that these are social artifacts, i.e., they arise out of the interactions within and between the various groups and social structures in which they are embedded. On this view, what is really interesting about organizational life is the myriad of processes through which the particularities of organizational situations arise, the role that language plays in such processes, and how all of this is tied up in the micro worlds of individual and group interactions. For the researcher, the message is clear: instead of seeking to capture the ‘‘essence’’ of ‘‘what things are’’ with a view to increasing our knowledge of the organizational world and thereby seeking to exercise greater degrees of control over it, the task is to understand the myriad of processes through which things come to be whatever it is we take them to be.

3 Foucauldian Discourse Analysis In a manner that is somewhat reminiscent of how autopoietic theory has been applied in the ﬁeld of organization studies, the perspective known as ‘‘discourse analysis’’ — based on Foucault’s work — has been appropriated for fulﬁlling a diverse range of tasks in the same ﬁeld, (see, e.g., Boje, 1994; Burrell, 1988; Clegg, 1994; Fox, 2000; Knights & Morgan, 1991; Townley, 1993). Fundamentally, and in an organizational context, these publications build upon and illustrate Foucault’s problematization of the relationship between language and action. This takes discourse analysis to be a practice which enables transformation not only in the ‘‘economy of discourse,’’ in terms of regimes of truth, but also ‘‘in the administration of scarce resources’’ (Foucault, 1972, p. 120). In other words discourse analysis is a political matter. It deals with the relationship between language and power relations and thereafter with access to material resources. Foucault speciﬁcally refuses ‘‘analysis couched in terms of the symbolic ﬁeld or the domain of signifying structures’’ in favor of an analytic model of war or battle: ‘‘relations of power, not relations of meaning’’ (Foucault, 1980, p. 114). Thus, he is interested in questions of what communication does rather than what it means. As Shapiro (1981) points out, Foucault’s concept of discourse is inevitably strategic. That is, irrespective of whether or not it is intended, discourse has political effects. The implication for organization scholars is that an analysis of organizational discourse is inseparable from an analysis of power relations. By extension, deﬁnitions of discourse are inseparable from wider social processes.

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In Foucault’s social theory, a range of key social concepts are incorporated within the notion of discourse. On this account, discourse is a way of constituting knowledge, together with the social practices, forms of subjectivity and power relations which inhere in such knowledge — and the relations between them (Weedon, 1987, p. 108). These social practices include language but they go beyond the verbal or linguistic, including dimensions such as space, time, and the body. Even when discourse analysis focuses on verbal texts — transcriptions or published versions of organizational discourse — language is never seen as operating in some kind of autonomous way or as representing some kind of simple index of organizational relationships. It is critical to remember that Foucault’s idea of power is productive: power is always seen in terms of ‘‘relations of power’’ (Foucault, 1983, pp. 217–218) rather than as a structural force separating ‘‘powerful’’ and ‘‘powerless’’ groups in some fundamental way.

4 Social Constructionism For many years, organizational research has drawn on the contributions of social psychology. However, following the 1966 publication of Berger and Luckmann’s seminal ‘‘The Social Construction of Reality,’’ one of the traditions in that discipline, social constructionism, has become particularly inﬂuential (see, e.g., Gergen, 1985, 1995; Shotter, 1997a, 1997b). Despite its social psychology origins, social constructionism is now an interdisciplinary approach that includes Wittgenstein’s (1953) work in philosophy; the contribution of ethnomethodologists such as Garﬁnkel (1984, 1996) and poststructuralists such as Derrida (1976) and Deleuze (2001). In essence, social constructionism moves beyond the traditional dualism that characterizes the debate between subjectivism and objectivism to focus on the process of social interchange. The (experiential) terms by which the world is understood are regarded as social artifacts, products of historically situated interchanges among people. Within the parent discipline of psychology, this represents a signiﬁcant move from an individual epistemology to a social one. In social constructionism, the focus on the individual ‘‘mind’’ is replaced by a focus on relationships. And because the locus of explanation shifts from the mind to the processes of human interaction, social constructionist research is mainly concerned with explicating those processes by which people account for the world in which they live (see, e.g., Gergen, 1995; Bayer, 1988). Its emphasis is on the way we negotiate meanings in our lives, and its practices stress how language fashions subjective experience. In organizational research, and mirroring developments in psychology, this relational emphasis on language has signaled a departure from an individualist perspective of organization to a communal one, or, to draw on Gergen’s (1999) terms ‘‘an alternative discourse to the discourse of the self-contained individual.’’ Harre and Gillet (1994) discuss how the pragmatics of everyday language serve to render certain types of social relationships more salient than others. Gergen also relates this

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dominant individualism to traditional views of knowledge that have invited people to see themselves as the center of their actions in a natural state of independence. Shotter (1995) highlights how, in the course of conversation, shared understandings are developed and negotiated through a ‘‘back and forth’’ process over time. So rather than regard organizational ‘‘objects’’ as representing something external, a representation of which is taken to exist in people’s heads, these are seen as far more ﬂuid: a consequence of ‘‘y everyday activities which continually shift and change’’ (Wetherall & Maybin, 1996). Not surprisingly then, it is the relational emphasis of social constructionism that guides organizational research. It also speciﬁes what we should take from the knowledge that such research generates. Social constructionism highlights that organizational accounts, including those of the researcher, are rhetorically formed. They do not offer ‘‘truth through method’’, instead they produce what are best described as ‘‘illustrations’’ (Shotter, 1995). Within social constructionism, a wide range of tools and methods can be used. However, in the end, methodological sophistication does not increase the validity of the resultant knowledge. Ultimately, organizational narratives are situated within a community of researchers which renders it intelligible. But while organizational researchers are primarily guided by such normative rules of shared intelligibility, they are invited to view these rules as culturally situated and thus always subject to critique and alteration (Gergen & Thatchenkery, 1996).

5 Autopoietic Theory and Process Research For the purpose of the argument developed here, I am taking ‘‘autopoietic theory’’ to be the body of work that, primarily, is associated with Maturana’s so-called ‘‘theory of the observer.’’ As has been outlined elsewhere in this volume (Maturana, 1970, 1980, 1988b, 1993; Maturana & Varela, 1980, 1987; Mingers, 1995; Whitaker, 1996), this brings into the spotlight people’s lived experiences and explanations as these are actively constructed in social networks. This represents a basic resonance between all three of Foucauldian analysis, social constructionism, and autopoietic theory. Despite their foundational differences, they agree that language is not an abstract symbolic system for communicating about an independently existing world, rather it is a relational phenomenon associated with the manner in which people live together in concrete settings. They further agree that language is the primary mechanism through which people construct their realities, and that all aspects of organizational life arise through a continual process of production and reproduction. Despite this, within the ﬁeld of organization studies, it is clear that the overall proﬁle and level of awareness of Foucauldian analysis and social constructionism is much higher than that of autopoietic theory. One could debate the reasons why this is the case. Perhaps, the biological origins of autopoietic theory betray a level of deterministic thinking that deters most organizational scholars from discovering that, in fact, this is not the case. Others, accustomed to thinking that the so-called ‘‘systems

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approach’’ provides an outdated and impoverished account of how organizations operate, might be discouraged for this reason. Perhaps, Maturana’s somewhat convoluted and repetitive writing style coupled with his seeming unwillingness to engage with other bodies of knowledge has not endeared him to a wider audience. In the event that there is any validity to these statements, it is clear that if autopoietic theory is to become more inﬂuential not only does more need to be written about it, and in the right places, but also its contribution needs to be seen to be distinctive, and, at least to some extent, complementary to these other processbased approaches. Any argument that autopoietic theory is ‘‘better’’ than other process-based approaches will surely fall on deaf ears. In any event, whether we are speaking of Foucauldian analysis or social constructionism, or, for that matter any other process approach, each one is coherent and valid in its own terms, and therefore has the right to be evaluated according to its own logic. It should also be allowed to set its own agenda. Thus, whereas Foucauldian analysis focuses particular attention, e.g., on the link between power and language, Maturana has only touched on this topic. Both Foucauldian analysis and social constructionism have generated numerous research methodologies and techniques, e.g., script, text and conversation analysis, text deconstruction, observational and clinical methods, longitudinal case research, to name but a few. As a result, social constructionism in particular has spawned a wide range of rich ethnographic accounts of various aspects of organizational life across a multitude of settings. Beyond somewhat limited forays into areas of therapeutic practice such as family therapy and psychotherapy (see Dell, 1982; Hoffman, 1988; Efren et al., 1990), autopoietic theory cannot claim to have made comparable methodological contributions. So what is distinctive about autopoietic theory and how might it best contribute? What does it do well that these other approaches do less well? It is only by showing that it can make a positive contribution to organizational research that scholars in that ﬁeld will be inclined to look beyond conventional stereotypes and take a ‘‘second look’’ at what this particular approach has to offer. In answering this question, my comments might come as a surprise to some, especially those (see, e.g., Birch, 1991) who have bemoaned Maturana’s painstakingly precise and almost bordering on the pedantic approach that he adopts in building and elaborating upon theoretical concepts. My own view is that although this approach can, at times, be irritating, overall it is less of a weakness and more of a source of advantage. Despite the provocative questions raised by Foucauldian analysis, despite the methodological sophistication, and despite the richly descriptive accounts and illustrations of the dynamics of organization life provided by social constructionism, signiﬁcant theoretical questions remain largely unanswered in these areas of study. For example, we have said that all three perspectives reject the idea that such objects exist independently. What is less clear is how, in particular contexts, objects come to be seen as ‘‘real’’ through the lived experience of the people involved. It is simply not clear how this process works. There is also a lack of clarity on how the concepts and distinctions that constitute ‘‘objects’’ relates to action and communal

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practice as well as to what exactly is meant by terms such as ‘‘discourse’’ and ‘‘narrative,’’ and how meaning emerges from these and how it can alter over time. Some might argue that such theoretical precision is unnecessary. Foucauldian scholars, e.g., often deliberately avoid operationalizing the term ‘‘discourse’’ arguing that this can be coded in different ways depending upon the research context (see Shapiro, 1981). Likewise in social constructionism, the term ‘‘discourse’’ is often used simply to refer to ‘‘y an instance of situated language use’’ (Burr, 1995). Such theoretical imprecision allows researchers to keep open their options. However, there is a ﬁne balance between keeping ones’ options open and having to suffer the consequences of excessive ambiguity and uncertainty regarding how one ought to proceed. A neophyte social constructionist, e.g., might reasonably ask speciﬁcally what aspects or ‘‘units of situated language use’’ ought to be the primary object of inquiry. To some extent, the answer to this question will derive from one’s choice of technique. Yet this may only serve to confuse further, since ordinarily research tools are designed to assist in eliciting knowledge of phenomena that are already clearly deﬁned. The point is that if the object of the exercise is to investigate some phenomenon then surely we need to have a clear idea about what it is that we are investigating before we kick-start the whole process. When examined in this light, Maturana’s often painstakingly precise theoretical elaboration of concepts is perhaps less problematic than it might otherwise appear to be. Surely, if it can address issues that are not adequately addressed elsewhere, or if it can plug theoretical gaps that are need of ﬁlling, then it has an important role to play. This raises the real possibility of there being a complementarity across the various approaches discussed here. Those who are already convinced that Maturana provides a compelling theoretical account of social behavior can see how these various propositions might be realized in practice through reference to the detailed organizational accounts provided by Foucauldian and social constructionist scholars. The latter, in contrast, might obtain greater conceptual clarity through engagement with Maturana’s thinking. Let us now consider some of the key areas where autopoietic theory can contribute in these terms.

5.1

The Nature and Consequences of Social Interaction

As we have seen, Foucauldian analysis and social constructionism draw attention to how meanings and what we observe as behavior arises out of a dynamic process of complex interactions of actors taking place in particular contexts. But how does this process of interaction actually work? What does the process of ‘‘social interchange’’ that social constructionists speak about actually involve? Unless we can answer this question how can we know why some interactions are clearly inﬂuential while others are not? What exactly is the ‘‘back and forth’’ mechanism between two or more actors that, so the argument goes, leads to accommodation? The point is that if ‘‘interactions’’ and the linguistic activity that takes place in the space between them are the main locus of study, it is surely important to be able to theorize possible

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answers to these sorts of questions. Yet neither social constructionism nor Foucauldian analysis does this. Instead of focusing on the mechanisms involved, they tend to focus mainly on articulating and teasing out how all of this is played out in various contexts. Space limitations preclude a detailed description of how autopoietic theory can contribute in dealing with the kinds of questions posed above. What can be said, however, is that the critical details that are involved in explaining social interactions are tied up with its twin concepts of ‘‘structure determinism’’ and ‘‘structural coupling.’’ In very simple terms, structure determinism conveys the idea that fundamentally what happens to a system — through interactions — depends upon it. On their own, external events cannot determine what happens to the system. It is the structure of system itself that determines whether an external event will trigger a reaction, and if so what the reaction will be. A simple example might be to consider the impact of the same anatomical movement being applied to the task of kicking a large rock, a football, and a balloon. Even though these entities may be roughly the same size, they have different structures, so the outcome will be markedly different. Depending upon its weight, the structure of the rock may ensure that a kick has no effect at all. In simple terms, this explains why in social settings, some interactions have zero effect and some have a greater effect than others. Witness how children at different ages, or people from different cultures, respond to the same verbal instructions, or how different employees in the same company respond differently to a particular management style or corporate communication. Building upon the idea of structure determinism, ‘‘structural coupling’’ refers to a situation in which it is possible to observe some degree of congruence or ‘‘ﬁt’’ between one system and another, or between a system and its ‘‘medium.’’ When there are recurrent interactions between two systems, structure-determined changes occur in both. In other words, the two structures change congruently each according to its own structure determinism. Through this process, the structure of the system, at any point in time, contains an imprint of previous interactions. As long as the system survives and as long as the interactions continue, structural change will occur. Structural coupling then is an important contributor in understanding process; speciﬁcally in understanding how human beings as employers/managers/employees, etc., relate to the myriad of formal and informal organizational milieu, i.e., personal relationships, informal social networks, conversational groups, structural units, etc., within which they are embedded. Structural coupling provides a way of thinking about, and a way of researching, how we are inﬂuenced by and how we inﬂuence these milieus, and, importantly on how, as a result of these interactions, we change over time. Structural coupling can also account for the ‘‘back and forth’’ accommodation between people about which social constructionists speak. However, there is an important caveat. When, in this context, we speak of ‘‘accommodation’’ we are referring an emerging and ongoing basic ‘‘ﬁt’’ between two systems. To illustrate the point, a simple example might be when an ill-ﬁtting shoe becomes more comfortable as subtle changes occur to both the shoe and the foot through continual wear. Another example might be when two individuals learn to accommodate one another

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even in situations of conﬂict and disagreement. Overtime, shared understandings that become the basis for ‘‘living together’’ might even emerge. However, the idea of structural coupling does not in any sense assume equality between the two interacting systems. The idea of structural coupling is as relevant to understanding the relationship between a bullying manager and a bullied employee (or vice versa) as it does to two individuals who interact on relatively equal terms. In both cases, there might be a ‘‘ﬁt’’ or accommodation between the two parties, but these are clearly of a very different nature. I shall return to this point shortly in discussing what autopoietic theory can contribute to the issues of the relationship between power, language, and action that is such a cornerstone of Foucauldian thinking.

5.2

Social Action — The Bedrock of Language

Shifting attention away from the basic mechanism of interaction between systems in general, let us now consider, more speciﬁcally, those that involve human beings and, in particular, which involve, in some manner or other, language. We have already noted some key assumptions about language that are shared by the three perspectives discussed here. First, it is the primary mechanism through which we construct or constitute and explain our worlds; second, it is something that we do with others, i.e., language develops intersubjectively; and third, it is linked to community practices, i.e., it is about actions and ‘‘doings,’’ it is not an abstract symbolic system of communication about a preexisting world. Conventional wisdom has it that the fundamental aspect of language is the speech act itself (see, e.g., Searle, 1983). In autopoietic theory, the fundamental aspect is action; vocabulary and speech are derived from action but come later in the piece. On Maturana’s view, languaging is associated with particular types of behavior, or particular ‘‘doings.’’ The most basic operation of this occurs when some entity does something on the consequences of an initial coordination of behavior between it and another entity which, by deﬁnition, must be structurally coupled, at least to some minimum degree (see Maturana, 1988a). In other words, there is a ‘‘coordination of a coordination of behavior.’’ From this basic behavioral coordination, further recursions of behavioral coordination result in language becoming increasingly complex and sophisticated. Thus, in human communities such as organizations, objects ‘‘arise’’ as tokens for highly speciﬁc behavioral coordinations. In his classes and public seminars, Maturana uses a number of examples to illustrate this process. The designation ‘‘taxi,’’ for instance, connotes the coordinated sequence of actions that are involved in carrying someone from one place to another (usually in a motor vehicle) in return for the payment of money. Similar coordinated actions underpin ostensibly simple designations such as ‘‘desk,’’ ‘‘computer,’’ as well as more complex ones such as ‘‘the annual retreat,’’ ‘‘the board,’’ ‘‘the retirement function,’’ ‘‘the brand,’’ etc. The same can be said of more abstract entities such as ‘‘fairness,’’ ‘‘equity,’’ ‘‘integrity,’’ ‘‘ethicality,’’ and so on. Across different organizations and different cultures, these are all anchored in highly speciﬁc and recursive coordinated behaviors.

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Having arisen through this process, we often lose sight of how such ‘‘objects’’ are anchored in actions. Thus: ‘‘y objects take place as distinctions of distinctions that obscure the co-ordination of actions that these co-ordinate’’ (Maturana, 1988a, p. 47). At this point in time, ‘‘the taxi,’’ ‘‘the CEO,’’ ‘‘the brand,’’ and everything else become entities ‘‘in-themselves,’’ which, of course, is how we tend to live them. Although language might appear to us as being about symbols that represent ‘‘things out there,’’ fundamentally it is about doing. Entities and objects correspond to ‘‘doings,’’ and language is a ﬂow of coordinations of coordinations of action. Language is about communal action and is a concrete phenomenon of the living. As we coordinate our behaviors in different ways; as, through various forms of linguistic interaction, we make new distinctions; and as we come up with new tokens for speciﬁc behaviors, we are continually weaving linguistic networks with other people.

5.3

Conversation, Discourse, and the Development of Meaning

So much for the basic building blocks of language. As observers, we do more than simply operate with simple one-word descriptors of ‘‘things’’; we combine these descriptors in highly creative communicative processes that, in the process literature, are variously known as conversation, narratives, discourse, ‘‘storylines,’’ ‘‘text,’’ and so on and so forth. Whereas Foucauldian analysis and social constructionism are somewhat vague on what exactly these things involve, Maturana has theorized the matter in some detail. On his account, the key concept is what he refers to as ‘‘conversation.’’ This involves two processes: ﬁrst, ‘‘languaging,’’ which we have already discussed; second, what he describes as ‘‘emotioning.’’ Emotioning refers to ‘‘bodily dispositions for actions’’ (Maturana, 1988a, p. 42). Such predispositions can be both individual and shared, so on this account, emotioning is a social construct as well as an individual one. Languaging and emotioning are ‘‘braided’’ processes, i.e., each process can affect the other. This can be seen in everyday conversations where the speciﬁc distinctions that people use can invoke an emotional response, and/or where people’s distinctions reﬂect the emotion or predisposition of the moment. This latter point is very important. The distinctions that we use to explain and understand our worlds are never isolated from bodily processes on the one hand and from social processes on the other. Importantly, as conversations ﬂow through an interweaving of distinctions and emotions, the interpretations and meanings that people attach to situations can alter. How people construe and feel about situations, and how they subsequently act in relation to a situation depends — at least to some extent — on the speciﬁc distinctions that they employ and on the ﬂow of their emotions from moment to moment. Typically, this is something that we recognize in our own daily life experiences and the basic idea is acknowledged in process research. How one goes about deﬁning (and then researching) whatever it is that generates ‘‘meaning,’’ however, is less clear. There are of course some exceptions where this matter has been theorized in some detail. A good example is Kelly’s so-called ‘‘Theory of Personal Constructs’’

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(Kelly, 1955). This has been inﬂuential amongst psychology-oriented social constructionism, and it has its own derivative research methodology known as the ‘‘repertory grid technique.’’ However, this is far from typical. Quite often, it simply appears to be a case of the researcher being encouraged to listen, read about, and/or observe a set of subjects and then make as much sense of this as he/she can. Autopoietic theory is much less ambiguous when it comes to providing guidance on what investigating meaning might involve. Indeed Maturana spells out in some detail what is involved in generating meaning out of a situation. This is tied up with his claim that human beings, by their very nature, have a predilection to explain as well as to experience situations. We do not have to explain experiences, but this is something that we tend to do, even from a very young age. He then goes on to develop an account of the structure and content of explanations, how these arise, how they develop and change over time, and how they relate to people’s ‘‘realities,’’ their rationalities and their beliefs on what, in any situation, constitutes appropriate or legitimate action. Beyond its biological aspects, to some extent autopoietic theory can be thought of as a sophisticated explanation of the process of explaining. Once again, space limitations preclude a detailed description of this here, so a highly abbreviated version will have to sufﬁce. In short, and irrespective of domain (i.e., science, religion, politics, professional life, intellectual life, daily life), the basic component of an explanation is a ‘‘generative process.’’ Basically this is an account along the lines of: ‘‘if y and y and y and y, then the result will be y.’’ In other words, in the experiential world of the observer, there is some linked and sequential process of events and activities that result in whatever experience is to be explained. From a research angle, this places the following in the spotlight: ﬁrst, the nature of this generative account including the various ‘‘objects,’’ ‘‘events,’’ ‘‘entities,’’ ‘‘actions,’’ and relationships among these; second, the ‘‘operational coherences’’ that, for the person concerned, ensure that the ‘‘explanatory domain’’ hangs together in a regular and predictable manner; and third, the basic preferences, biases, and predilections that account for why someone will accept one generative process as providing an adequate account of an experience (science, e.g.) while at the same time, he/she will reject others (witchcraft, e.g.). With this set of theoretical propositions, it is somewhat less of a mystery as to what ‘‘investigating meaning’’ involves. Beyond outlining a more useful set of theoretical propositions that assist in elaborating on what ‘‘making meaning’’ might involve, Maturana’s description of the social context is similar to that discussed elsewhere although a different language is used. For example, he uses the term ‘‘consensual domain’’ to describe the (social) networks of structural coupling that are the site for conversations. In these contexts, people learn their emotioning, their languaging, and their explanations with people to whom they are structurally coupled, and, through recurrent interactions, structural patterns can become conserved. Autopoietic theory makes another very important theoretical contribution on this topic. Social constructionism and Foucauldian invites us to consider the organization as comprising a multitude of organizational realities and communicative processes,

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which although relatively autonomous as discourses may overlap and permeate each other. What is less clear is how this ‘‘overlapping’’ works. Maturana would agree that in organizational settings, many of our structural couplings are with other members of the organization and many of the conversations that take place there are relatively independent. Some may even be unique to the speciﬁc organizational context. However, as human beings, we all have multiple structural couplings and we participate in an inﬁnitely large number of different conversations. Each one of these has its own braided ﬂow of distinctions and emotions that have been learned through recurrent interactions over time and which alter subject to the dynamic ﬂow of the conversation. This means that just as thoughts and descriptions within a single conversation are subject to change depending upon the ﬂow of the conversation, they can also alter as the observer shifts from one conversation to another. But how do these conversations interact? Visually this can be explained using the image of a wheel where there is a hub and a large number of intersecting spokes. The spokes represent intersecting conversations; the hub represents the ‘‘bodyhood’’ of the observer. The mechanism is then very simple: if the structure of bodyhood allows it, each conversation — subject to the principle of structure determinism — will trigger structural change. This then alters the way the observer participates in other conversations. Through this theoretically very simple process, we can explain a number of important observations, e.g., how one conversation can affect another, how language can create change and learning, why some conversations are more powerful than others, and so on. This mechanism also draws attention to another possible theoretical contribution of autopoietic theory which I shall now consider. This has to do with the relationship between the individual and the social.

5.4

The Individual and the Social

As we have seen, the various bodies of thought discussed here assert that the process of creating meaning is primarily a social activity. For social constructionism, this has signaled a major departure from an individualist perspective of meaning to a communal one. This contrasts with the strongly individualistic emphasis in many Western cultures and traditional views of knowledge that have invited people to see themselves as the center of their actions in a natural state of independence. However, while this shift in emphasis from the individual to the social is understandable, if it is taken to imply that the individual is relatively unimportant, it does raise some interesting questions. For example, if we accept that organizational and other communication processes do intersect and affect one another, then we have to explain how this happens. On their own, commonsense logic dictates that communication processes such as conversations, narrative, and text cannot ‘‘talk to one another’’; so there has to be some mediating mechanism. That can only be the individual observer. Moreover, even if we do assert the social basis of language, something, other than itself, has to make language possible in the ﬁrst place and

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‘‘make it happen.’’ Traveling from A to B in a motor vehicle cannot be explained simply through reference to the running of the car engine. The journey traveled can only be explained through reference to the totality of the vehicle and how it intersects with and relates to the layout of roads and the geographical terrain. Yet without the engine, the vehicle goes nowhere. The abrupt raising of one’s arm to hail a taxi cannot be explained through reference to the anatomic movements involved; ‘‘hailing a taxi’’ is a socially deﬁned act that takes place in a particular context. Yet without the anatomical movement of the arm there is no ‘‘message’’ sent to the taxi driver. The same can be said of language and of all human activity that occurs through this mechanism. Without an advanced nervous system, there are no reﬂections; without a physical capacity to vocalize different combinations of sounds, there is no spoken language. For autopoietic theory then, arguments about whether human meaning is mainly individual or mainly social are misguided; the individual and the social domains are equally important. As Maturana (1988a, p. 63) puts it: ‘‘As living systems, we exist in two non-intersecting phenomenal domains: the domain of our realization in our bodyhoods (the domain of physiology) and the domain of behavior (the domain of our interactions as totalities).’’ The distinctive contribution of autopoietic theory is not simply to say that both of these domains count; it is also to explain the nature of the relationship between them. Although the individual and social domains are separate and independent phenomenal domains, they are nonetheless inextricably intertwined as components of a larger totality. As Maturana (1997, p. 2) puts it: ‘‘y bodyhood and manner of operating as a totality are intrinsically dynamically interlaced; so that none is possible without the other, and both modulate each other in the ﬂow of living. The body becomes according to the manner (in which) the living system operates as a whole, and the manner (in which) the organism operates as a whole depends on the way (in which) the bodyhood operates.’’ On this basis, we can say that people’s experiences, explanations, and their behavior more generally ‘‘belong’’ to the social domain. However, what happens in that domain is made possible by an individual bodyhood which also delimits the range of social possibilities. This point is worth emphasizing since organization studies has not been particularly inclined to grapple with the biological underpinnings of behavior. Yet as Maturana reminds us, everything that human beings do, including how we participate as social beings, is done as the kind of living individual biological systems that we are. On that basis, biology cannot fail to have an extremely important ‘‘generative presence’’ even if this is far from a simple deterministic one. It follows then that when someone has any sort of relational encounter, e.g., when they reﬂect on an issue (in isolation or with others), or when they participate in a conversation, there are certain parameters that govern the range of possible outcomes. At a very basic level, the structure of the nervous system delimits the range of possibilities as do the physiological and emotional predispositions of the moment. Although what happens during the relational encounter depends upon the social dynamics of that context, nonetheless the impact of bodyhood can be profound. There are times, e.g., when what we see, what we experience, and hence what we do is very closely linked with the ﬂow of our emotions and other bodily predispositions.

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Biology then impacts upon but does not determine what happens in the relational domain. At the same time, relational encounters impact upon but do not directly imprint themselves upon biology. At one extreme, an acrimonious exchange between two people may increase the blood pressure of the people concerned. Longer term, if the exchanges are repeated, it could trigger cardio-vascular disease; one of the participants might even suffer a heart attack and die. At the other extreme, using various meditation techniques and through processes of ‘‘self talk,’’ someone might learn how to reduce muscular and nervous tension to good long-term effect. In both cases, there is a structural transformation of the system in line with changes in relational circumstances and in the medium itself. Indeed this is the basis for evolutionary development and learning (see Maturana, 1988a, p. 74; Winograd & Flores, 1987, pp. 44–47). However, such changes are always subject to the system’s structure determinism. This is a never-ending iterative process since the changed structure then generates the parameters which govern subsequent social behavior. This elaboration of the nature of the relationship between the individual and the social is a highly distinctive feature of autopoietic theory.

5.5

Power

Let me now turn to what can be said about autopoietic theory in the light of the strong emphasis that is placed on power in Foucauldian discourse analysis. While Foucault’s fundamental intertwining of discourse and power is not something that Maturana discusses per se, the overall conceptual framework, key aspects of which have been outlined above, does open theoretical space for such an explanation. In simple terms, the link between power and meaning hinges ﬁrst on the proposition that conversations — and languaging and emotioning more speciﬁcally — occur in networks of structural coupling. Recall that this concept asserts a dynamic form of adaptation or mutual adjustment between a living system and a medium, or between two or more living systems. In this case, the most obvious and relevant example is the structural coupling between two or more human beings. However, the same logic could be applied, as it is in Foucault’s writings, to the relationship between different ﬁelds of inquiry, disciplines, and institutions. Irrespective of the context, it would be erroneous to presume that this coupling is an equal one, so the potential for a power imbalance always exists. In the vernacular it could be said that one system ‘‘calls the shots,’’ so where there is ‘‘adjustment,’’ inevitably one party will adjust more than the other. Such logic applies to all interacting systems; potentially, there is always one party that is relatively dominant, one that is relatively subservient. Thereafter, recall that conversations reﬂect the exigencies of the structural couplings; powerful actors are able to initiate some conversations and close down others; powerful actors can choose to invite or not invite others to participate (i.e., become structurally coupled); powerful actors can seize control over the distinctions that are employed and control the ‘‘emotional ﬂow’’ by ‘‘controlling the

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agenda’’ and otherwise shaping the various predispositions that open up some possibilities and close down others. Finally, since people’s meanings and explanations arise through the various couplings and the conversations that take place there, it is logically quite straightforward with autopoietic theory to craft a link between power, meaning, and knowledge. Although this inextricable linking of power and meaning needs to be taken very seriously, one wonders why Foucauldian discourse analysis elevates power relations to the level that it does. Recall that conversations involve a braiding of emotioning and languaging, which means that people’s distinctions reﬂect emotions more generally, not predispositions to subjugate, dominate, and/or control in particular. In that sense one could claim that it is emotion that is the primary ‘‘always-already’’ condition; it is not, as Foucault appears to suggest, power. As I have already said, how human beings think, how they act, and what they do partly depends on their emotional ﬂow, and some of this reﬂects biological dynamics. At the same time, emotional predispositions also arise in coexistence with others as we go about living together. For the researcher who seeks to reveal the implicit predilections and preferences that are embodied in organizational cultures, policies, and practices, the possibility arises that, alternatively, they may be grounded in emotions such as love, mutual respect, friendship, indifference, or in some other disposition. The researcher does not necessarily have to be swayed by the profound pessimism of Foucault which points toward the negative and hegemonic impact of totalizing managerial discourses. Foucault’s seminal work on prisons reminds us that many of our organizations do indeed share some of the characteristics of such institutions. But some do not, and there are no compelling reasons why organizational researchers should formulate projects on the premise that power is at the root of everything.

6 Conclusion Despite its popularity in some quarters, autopoietic theory has not achieved the sort of proﬁle in the ﬁeld of organization studies that one might have expected. Typically it has been employed to shed light on speciﬁc issues that are of interest to a limited number of organizational scholars. To date it has not been opened up to a wider potential audience through being aligned with a more general organizational research paradigm. This chapter has suggested that the recent turn toward a process and language-based approach in organizational studies provides fertile ground for autopoietic theory to play a more central role. Speciﬁcally, I have argued that it can plug some important theoretical gaps that exist in this literature. In particular, the chapter has sought to demonstrate that autopoietic theory can be helpful in specifying a number of important things more precisely than is the case elsewhere. These include: the process through which human beings interact, the process through which they inﬂuence one another, and through which they change over time; the action and behavioral basis of language; the nature of conversations and dialogue and the role that these play in generating meaning; and the relationship between the individual

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and the social. In addition, autopoietic theory is able to contextualize the relationship between power, meaning, and action by showing how power may not always be the dominant emotional predisposition that governs social relations.

References Barrett, F. J., Thomas, G. F., & Hosevar, S. P. (1995). The central role of discourse in largescale change: A social construction perspective. Journal of Applied Behavioral Science, 31(3), 352–372. Barry, D., & Elmes, M. (1997). Strategy retold: Toward a narrative view of strategic discourse. Academy of Management Review, 22(2), 429–452. Bayer, B. (1988). Re-enchanting constructionist inquiries. In: B. Bayer & J. Shotter (Eds), Reconstructing the psychological subject (pp. 1–20). London: Sage. Beer, S. (1981). Brain of the ﬁrm (2nd ed.). Chichester: Wiley. Birch, J. (1991). Re-inventing the already punctured wheel: Reﬂections on a seminar with Humberto Maturana. Journal of Family Therapy, 13, 349–373. Blackler, F., Crump, N., & McDonald, S. (2000). Organizing processes in complex activity networks. Organization, 7(2), 277–300. Boje, D. (1994). Stories of the storytelling organization: A postmodern analysis of Disney as Tamara-land. Academy of Management Journal, 38(4), 997–1035. Burr, V. (1995). An introduction to social constructionism. New York, NY: Routledge. Burrell, G. (1988). Modernism and PostModernism II — The Contribution of Michel. Chia, R., & Holt, R. (2006). Strategy as practical coping: A Heideggerian perspective. Organization Studies, 27(5), 635–655. Chia, R., & MacKay, B. (2007). Post-processual challenges for the emerging strategy-aspractice perspective: Discovering strategy in the logic of practice. Human Relations, 60(1), 217–242. Clegg, S. (1994). Weber and Foucault: Social theory for the study of organizations. Organization, 1(1), 149–178. Cunliffe, A. L. (2001). Managers as practical authors: Reconstructing our understanding of managerial practice. Journal of Management Studies, 38(3), 351–371. Deleuze, G. (2001). Difference and Repetition. London: Athlone. Dell, P. (1982). Family therapy and the epistemology of Humberto Maturana. Family Therapy Networker, 6(4), 39–41. Dell, P. (1985). Understanding Bateson and Maturana: Toward a biological foundation for the social sciences. Journal of Marital and Family Therapy, 11(1), 1–20. Derrida, J. (1976). Of grammatology. Baltimore, MD: John Hopkins University Press. Eden, C. (1992). Strategy development as a social process. Journal of Management Studies, 29(6), 799–811. Efran, J., Lukens, M., & Lukens, R. (1990). Language, structure, and change-frameworks of meaning in psychotherapy. New York, NY: W.W. Norton and Co. Inc. Foucault, M. (1972). The archaeology of knowledge. London: Routledge. Foucault, M. (1980). Truth and power. In: C. Gordon (Ed.), Power/knowledge: Selected interviews and other writings, 1972–1977 Michel Foucault. New York: Pantheon. Foucault, M. (1983). Afterword: The subject and power. In: H. Dreyfus & P. Rabinow (Eds), Michel Foucault: Beyond structuralism and hermeneutics (pp. 208–226). Chicago, IL: University of Chicago Press.

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Fox, S. (2000). Communities of practice, Foucault, and actor network theory. Journal of Management Studies, 37(6), 853–868. Garﬁnkel, H. (1984). Studies in ethnomethodology. Cambridge, MA: Polity Press. Garﬁnkel, H. (1996). Ethnomethodology’s program. Social Psychology Quarterly, 59(1), 5–21. Gergen, K. (1985). The social constructionist movement in modern psychology. American Psychologist, 40(3), 266–275. Gergen, K. (1992). Organization theory in the postmodern era. In: M. Reed & M. Hughes (Eds), Rethinking organization (pp. 207–226). London: Sage. Gergen, K. (1995). Global organization: From imperialism to ethical vision. Organization, 2, 519–532. Gergen, K. (1999). Social constructionism and relational action. Theory and Psychology, 9, 113–115. Gergen, K., & Thatchenkery, T. (1996). Organization science as social construction: Postmodern potentials. Journal of Applied Behavioral Science, 32(4), 356–377. Goolishian, H. A., & Winderman, L. (1988). Constructivism, autopoiesis and problem determined systems. Irish Journal of Psychology, 9(1), 130–143. Harre, R., & Gillet, G. (1994). The discursive mind. Thousand Oaks, CA: Sage. Hoffman, L. (1988). A constructivist position for family therapy. Irish Journal of Psychology, 9(1), 110–129. Hosking, D. M., & McNamee, P. (2002). The social construction of organization. Copenhagen: Liber Herndon. Kay, R. (1997). Applying autopoiesis to the facilitation of organizational change. Australian and New Zealand Systems Conference, Brisbane. Kay, R. (2001). Are organizations autopoietic? A call for a new debate. Systems Research and Behavioral Science, 18(6), 461–477. Kelly, G. (1955). The psychology of personal constructs: A theory of personality. New York, NY: Norton. Knights, D., & Morgan, G. (1991). Corporate strategy, organizations, and subjectivity: A critique. Organization Studies, 12(2), 251–273. Knights, D., & Morgan, G. (1995). Strategy under the microscope: strategic management in IT in ﬁnancial services. Journal of Management Studies, 32(2), 191–214. Krogh, G. V., & Roos, J. (1998). Knowing in ﬁrms: Understanding, managing and measuring knowledge. London: Sage. Luhmann, N. (1986). The autopoiesis of social systems. In: F. Geyer & J. V. D. Zouwen (Eds), Sociocybernetic paradoxes: Observation, control, and evolution of self steering systems (pp. 172–192). London: Sage. Luhmann, N. (1990). The cognitive program of constructivism and a reality that remains unknown. In: W. Krohn, G. Kuppers & H. Nowotny (Eds), Selforganization: Portrait of a scientiﬁc revolution (p. 270). Dordrecht: Kluwer. Maturana, H. (1970). Biology of cognition. In: H. Maturana & F. Varela (Eds), Autopoiesis and cognition. Dordrecht: Reidel. Maturana, H. (1980). Man and society. In: F. Benseler, P. M. Hejl & W. K. Kock (Eds), Autopoiesis, communication, and society: The theory of autopoietic systems in the social sciences (pp. 11–32). Frankfurt: Campus Verlag. Maturana, H. (1988a). Reality: The search for objectivity or the quest for a compelling argument. Irish Journal of Psychology, 9, 25–82. Maturana, H. (1988b). Ontology of observing: The biological foundations of self consciousness and the physical domain of existence. American Society for Cybernetics, Felton, CA. Available at: http://www.inteco.cl/biology/ontology/index.htm Maturana, H. (1993). Videotaped seminar on language and cognition. Melbourne: St. Kilda.

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Maturana, H. (1997). Metadesign. Available at: http://www.inteco.cl/articulos/metadesign.htm Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht: Reidel. Maturana, H., & Varela, F. (1987). The tree of knowledge — The biological roots of human understanding. Boston, MA: Shambhala. McKinlay, A., & Starkey, K. (1998). The ‘velvety grip’: Managing managers in the modern corporation. In: A. McKinlay & K. Starkey (Eds), Managing Foucault (pp. 111–125). London: Sage. Mingers, J. (1992). The problems of social autopoiesis. International Journal of General Systems, 21(2), 229–236. Mingers, J. (1995). Self-producing systems — Implications and applications of autopoiesis. New York and London: Plenum Press. Morgan, G. (1986). Images of organization. Newbury Park, CA: Sage. Robb, F. (1989). The application of autopoiesis to social organization: A comment on John Mingers reply. Systemic Practice and Action Research, 2(3), 353–360. Searle, J. (1983). Intentionality: An essay on the philosophy of mind. Cambridge: Cambridge University Press. Shapiro, M. (1981). Language and political understanding: The politics of discursive practices. New Haven, CT: Yale University Press. Shotter, J. (1995). The manager as a practical author. In: D. Hosking, H. Dachler & K. Gergen (Eds), Management and organization: Relational alternatives to individualism (pp. 125–147). Aldershot, UK: Avebury. Shotter, J. (1997a). Dialogical realities: The ordinary, the everyday, and other strange new worlds. Journal for the Theory of Social Behavior, 27(2/3), 345–357. Shotter, J. (1997b). The social construction of our inner selves. Journal of Constructivist Psychology, 10, 7–24. Teubner, G. (Ed.) (1984). Autopoiesis and the law. Berlin: de Gruyter. Townley, B. (1993). Foucault, power/knowledge, and its relevance for human resource management. Academy of Management Review, 18(3), 518–545. Weedon, C. (1987). Feminist practice and poststructuralist theory. London: Blackwell. Wetherall, M., & Maybin, J. (1996). The distributed self: A social constructionist perspective. In: J. Stevens (Ed.), Understanding the Self (pp. 180–219). London: Sage. Whitaker, R. (1996). The observer web: The internet nexus for autopoiesis and enaction. Available at: http://www.informatik.umu.se/Brwhit/AT.html Winograd, T., & Flores, F. (1987). Understanding computers and cognition. Massachusetts: Addison-Wesley. Wittgenstein, L. (1953). Philosophical investigations. Oxford: Blackwell. Wood, M., Bakken, T., et al. (2006). Process thinking in organizations. Bergen: European Group on Organization Studies. Zeleny, M., & Hufford, C. (1992). The application of autopoiesis in systems analysis: Are social systems also autopoietic systems?. International Journal of General Systems, 21(2), 145–160.

Chapter 9

An Autopoietic Understanding of ‘‘Innovative Organization’’ Tore Bakken, Tor Hernes and Eric Wiik

1 Introduction To be innovative is increasingly considered an imperative in modern society. The motto seems to be ‘‘the more, the better,’’ which is echoed in writings about phenomena such as ‘‘disruptive technologies’’ (Christensen, 1997), ‘‘disruptive innovations’’ (Christensen & Raynor, 2003), or radical innovation (Stringer, 2000; Leifer et al., 2000). Such phenomena are typically held up against ‘‘anti-innovative’’ phenomena, for example, ‘‘disruptive’’ is contrasted with ‘‘continuous,’’ and ‘‘radical’’ is contrasted with ‘‘incremental.’’ Distinctions drawn between being more or less innovative derive in part from studies that are based on stable causal factors that explain why some organizations happen to be more innovative than others. A common denominator of many studies is that the driver of innovation lies with the entrepreneur, the manager, or some other phenomenon of endurable qualities, such as the strategy or the product. Schumpeter assumed that entrepreneurs achieve success due to their unique abilities (Fagerberg, 2006). Tidd, Bessant, and Pavitt (2001) in their book Managing innovations place much of the success of innovation processes with managerial competencies. Hargadon and Douglas’ (2001) analysis of Edison’s success in bringing on electric light in the face of strong forces working for the preservation of gas as a source of lightning emphasizes Edison’s design strategy rather than his unique entrepreneurial abilities. Luhmann’s autopoietic theory offers a theory without a priori deﬁned drivers of novelty. Instead focus is on how the properties of the communicative system inﬂuence the ways in which it engages with novelty. The analysis assumes that the system continuously has to reproduce its central features while projecting expectations into a (possible) future. The analysis does not co-thematize the individual (such as the entrepreneur, inventor, or the manager), which leaves the theory indeﬁnite in relation Autopoiesis in Organization Theory and Practice Advanced Series in Management, 169–182 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006010

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to the question why new structures appear. Such assumptions has led to claims that Luhmann’s theory is relevant only to the study of routines and not to innovative processes (cf., Beckert, 1997, p. 347), and that it prevents a satisfactory understanding of the phenomenon of innovation (Joas, 1992; Giddens, 1996). We would argue differently, and say that autopoietic theory offers a way of conceptualizing how systems reproduce themselves in the face of novelty, further that it is the expected possibility of connecting to novelty that drives systems forward. The possibility of novelty is a central part, both of reproducing central features, and producing features for future operations. Possibilities for novelty arise as systems, as part of their recursive reproduction, draw distinctions amid a changing environment. The system reproduces itself recursively, pointing forward to possible connections, and at the same time connecting to previous operations. It is in this sense that a system may be understood as a ‘‘historical machine’’ (cf., von Foerster, 1993), or a ‘‘system-in-an-environment-with-a-history’’ (cf., Luhmann, 2005, p. 25). We would argue that an autopoietic theory of organization is in fact also a theory of innovation. Without the possibility of novelty, autopoietic organization is hardly possible. A second argument we make in this chapter is that, contrary to much of the literature on organization and innovation, an autopoietic view does not consider the degree to which innovation takes place. Instead it considers how the nature of communication shapes expectations, thus inﬂuencing the search for novelty. If we assume that different functions within an organization operate according to different modes of communication, we may come to a different understanding of how the organization engages with novelty. Key to this understanding is that different organizational functions operate with different degrees of redundancy in their communication.

2 Key Concepts We base our discussion on selected concepts from Luhmann’s autopoietic theory. A key concept is expectations which Luhmann developed from early works in the psychological and sociological literature. One feature of self-referential systems is that they generalize meanings of their elements into expectations: The concept of expectations points to the fact that the referential structure of meaningful objects or themes can only be used in a condensed form. Without this condensation the burden of selection would be too great for connecting operations. Expectations are formed by the intervening selection of a repertoire of possibilities, by whose light one can orient oneself better and, above all, more quickly. (Luhmann, 1995, p. 96)1 1

What Luhmann is looking for is how to generalize expectations, but not in Parsons’ fashion where there is a strong link between norms and expectations which stabilizes the expectations. For Luhmann generalizing constrains what is possible, while making visible other possibilities. In this way, Luhmann applies a new term which he calls’’redundant complexity’’ as an alternative to the more well-known term ‘‘organized complexity.’’

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Expectations serve to drive the system forward while connecting the system with its past. Expectations are proper to the system and rooted in its past, they take place in the present; what were at one point in time expectations for the future become past experiences at a later point in time. They do not, however, contain any certain predictions, expectations are tentative. As Luhmann (1995, p. 110) suggests, a social system is an autopoietic system and hence there is no basal certainty about states or prediction of behavior to be built on. There is not control of what will happen. Social systems operate in a world of double contingency, which implies that the system will never have complete knowledge of what the reactions will be to its own actions.2 The problem of double contingency was raised by Parsons (1951), who worked from the assumption that it would be solved by shared norms between actors.3 Luhmann argued that the assumption of shared norms was inadequate for explaining how systems deal with double contingency. Under uncertainty, instead, systems stabilize expectations (Luhmann, 1995, p. 110), and, depending on the outcomes, they will correct their actions within their own repertoire of behavior. A second concept is that of communication.4 A social system, being based on actions, needs communication in order to sustain itself, as communication provides meaning to actions. In Luhmann’s own words, communication serves to inundate the system (of actions) with meaning. It is a kind of ‘‘self-excitation’’ (Luhmann, 1995, p. 171) that helps the system sort order from noise, and thus cope with the challenges posed by double contingency. Not only does communication help the system cope with the problem of contingency, but it also sensitizes the system to unexpected and disturbing events within the repertoire of understanding that constitutes the system (Luhmann, 1995, p. 172). It is the ways in which communication sensitizes the system to unexpected and disturbing events that in this chapter we see as characterizing the system’s engagement with novelty, which is sometimes called ‘‘innovation’’ in the organization literature. A third concept that we draw upon is that of redundancy. In Luhmann’s conception, social systems are seen as unlikely phenomena in the sense that they have to constantly produce and reproduce themselves. They are based on operations which disappear as soon as they are produced, and consequently depend on their own ability to reproduce themselves. The system’s ability to reproduce itself lies not in repetition, but rather in connectivity. In an uncertain world, connectivity entails connecting to something that is yet to occur, and then cope with the consequences of that

2

Although Luhmann concedes that a completely indeterminate situation (i.e., ‘‘pure’’ double contingency) never occurs in our societal reality (Luhmann, 1995, p. 119). 3 In fact, this was also offered as an explanation in systems theory (Emery & Trist, 1965) for what happens between organizations and their environments, when uncertainty and the rate of change become too high for the organization to keep pace with the environment. 4 Communication consists of information, utterance, and understanding. Information is understood as ‘‘a difference that makes a difference’’ (Bateson, 1972); utterance refers to the how and why of the communication; and understanding is the making of a distinction between information and utterance.

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occurrence. The ability of a system to cope with the unexpected depends on the degree of redundancy that it contains. Redundancy is a concept from systems theory that Luhmann applied to communication. Redundancy implies a surplus of informational possibilities and thus protects the system from the danger that something will be lost (Luhmann, 1995, p. 171). Redundancy may be interpreted as a surplus of connectivity enabled by communication. A fourth concept that we draw upon from Luhmann’s autopoietic theory is that of themes. Social systems, according to Luhmann, develop thematic structures to which contributions are made. Themes deﬁne what may or may not be acceptable contributions. Thus, themes deﬁne what is considered important or not important. In this chapter we apply the notion of themes to different areas of activity within an organization. More precisely we work from the idea that areas of strategy, administration, and technology development represent examples of different themes, each exhibiting different characteristics of communication.

3 The Problem of the ‘‘Innovative Organization’’ Literature With few exceptions (e.g., Van de Ven, Polley, Garud, & Venkatamaran, 1999), works on innovation and organization tend to consider innovation to take place to a smaller or lesser extent. Thus, some writers distinguish ‘‘radical innovations’’ (Leifer et al., 2000) and others distinguish ‘‘disruptive technologies’’ (Bower & Christensen, 1995) from the supposedly more mundane attempts made at exploring novelty in organizations. Thus, organizations are seen as being more or less innovative. Whereas some organizations are seen to be endowed with qualities enabling them to beat the competition by staying ahead, others are characterized as less daring and willing to engage with novelty. Such views of innovation ﬁnd their basis in an ontological view of ‘‘organizations’’ that has dominated in organization theory for decades. This assumption in organization theory has given rise to views of innovation as being a matter of degree, thus paving the way for assuming that one could correlate basic features of an organization with its ability to innovate. Based on older forms of systems theory, traditional organization theory has assumed a boundary between organization and environment, which has been assumed to be more or less open. The underlying assumption has been that the environment and the organization act upon one another from either side of this ‘‘line’’ or organizational boundary. But more often than not, the organization has been understood to be the passive part that has to align itself to the demands in the environment (Weick, 1979; Tsoukas & Chia, 2002). Thus, expressions such as ‘‘strategic ﬁt’’ have made their entry into the terminology of organizational understanding. The view has implications for how change is understood and studied. In particular, correlation presupposes correlation of states of the organization and the environment (Hernes, 2008). In other words, the state of the organization is compared to the state of the environment at the same time. Consequently, organizations in rapidly changing environments have been expected to follow suit if they are not to be threatened with extinction.

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To be sure, an autopoietic view also accords the environment inﬂuence in shaping the organization. There is little doubt that different environments stimulate the emergence of different types of organizations. A rapidly changing environment is not very likely, for example, to stimulate the emergence of organizations with strong bureaucratic features. One of the main differences between traditional organization theory and the autopoietic view, however, lies in the conceptualization of the dynamics of the organization–environment interface. An autopoietic view assumes that the organization observes itself and its environment while performing distinctiondrawing operations. In other words, the organization is accorded choice among the possibilities offered by the environment. The possibilities offered by the environment may differ in terms of resource scarcity or information surplus (Luhmann, 2000, p. 35). As opposed to the mainstream views in organization theory that organizations are more or less open to the environment, autopoietic theory is based on the idea that an organization must be operationally closed in order to be cognitively open. Only by differentiating itself from, and closing itself off, from the environment can the system observe the environment as something different; only because the system is closed (i.e., other than the environment) can it be cognitively open toward this environment. The system draws distinctions, and observes itself and the environment from its own criteria. Distinction-drawing operations are self-referential in the sense that they are oriented toward the organization’s self-productive features. But at the same time the distinction drawing enables the organization to consider alternative possibilities toward which it may connect. It is in this ‘‘dual’’ situation where the organization both observes the possibilities for reproduction and possibilities for connecting to something different, that the organization reafﬁrms, or rediscovers, itself. Luhmann (1987, p. 320) formulates it thus: The experience of something as new and the assertion that something is new, consequently only indicates a decision to use up to now redundant possibilities for structural formation. The label ‘‘new’’ is thus an element of the system’s self description. It emphasizes discontinuity in order to demolish traditions and to reorganize connective capability. Novelty, then, is always about a system’s relation to itself, and therefore also to the old. (Our translation)

This is not, as assumed in traditional organization theory, a situation where the environment imposes unilaterally its demands on the organization. On the contrary, the organization ‘‘rediscovers’’ itself by engaging with the possibilities offered by its environment. This means — and this is relevant to the question of innovation — that the irritations potential in the organization’s environment increases. The concept of irritation is related to that of information, but quite the same. While information is a deﬁned surprise, an irritation is as an undeﬁned surprise that is based on the environment, yet remaining a product of the system itself. When the ﬁre-alarm goes off, it is not the surroundings that are irritated but the ﬁre brigade. The question poses itself thus: Where is the ﬁre? And how extensive is it? At the same time, following from the above quote, novelty is also about the system’s relationship to what it has been, that is, to its past. From such a viewpoint it does not really make sense to ask about the degree of novelty that the organization

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engages in. Instead it makes more sense to ask how the organization engages with the novelty that presents itself as a possibility, but without ‘‘losing itself.’’ This is where literature on ‘‘innovative organization’’ is in need of alternative thinking. It is noticeable how the literature describes organizations as exhibiting basic features making them more or less innovative. Thus, for example, bureaucratic features are seen as making an organization generally adverse to innovation. An early contribution in this respect is Victor Thompson’s (1965) paper on how bureaucratic features of organizations correlate negatively with their capacity for innovation. The ability of engaging with novelty has also been correlated with traits of an organization’s culture (e.g., Peters & Waterman, 1981; Tidd et al., 2001), absorptive capacity (Cohen & Levinthal, 1990; Zahra & George, 2002), or dynamic capabilities (Eisenhardt & Martin, 2002; Winter, 2002).

4 Communication and Expectations Consider the following example from a much cited example in the innovation literature. It relates to the development of the 3M Post-it note. In the mid-1970s Art Fry, technologist at 3M, was a tenor in his church’s choir; to his frustration, his scrap paper bookmarks kept falling out of his hymnal when he leafed through it. He tried several alternatives without much luck. Hymnals have very thin paper that is easily ripped. The solution called for bookmarks coated with adhesive strong enough to keep them in place when the reader leafed through the hymnal, yet not so strong as to rip the page when they were removed and repositioned. Moreover, the bookmarks needed to be sufﬁciently durable to be used several times. Coincidentally, others at 3M were then attempting to produce several types of adhesives for self-adhesive applications. Indeed, 3M research scientist Spence Silver had, in 1968 (several years earlier) while looking for ways to improve 3M’s acrylate adhesives for tapes, developed an adhesive that was both strong enough and ‘‘respositionable.’’ However, 3M did not see a commercial application for such adhesive. In a moment of pure ‘‘Eureka,’’ Fry realized that Silver’s adhesive could make for a wonderfully reliable bookmark (3M webpage). Eventually, Silver’s adhesive on paper — the Post-it — solved Fry’s problem (cf., also Hernes, 2008). Mainstream literature would tend to attribute the 3M story to the ability of Fry to search for novel solutions to a problem, or to the organizational structure and management culture at 3M that allow for experimentation. In either case there is an assumption of ‘‘drivers’’ of the process which explain how a decisive breakthrough such as the Post-its note can happen. An autopoietic understanding of such an example, however, would leave out the idea of stable drivers and instead focus on how the communication inﬂuences its exploration of novelty through expectations. Thus, we abandon the idea that innovation is driven by entrepreneurs or managers, and we shift to a level of analysis where the communication is in focus. Expectations that exhibit a high degree of precision are what Luhmann refers to as claims. In an unpredictable world such expectations may lead to disappointment when

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they are not fulﬁlled. On the other hand, if fulﬁlled and hence conﬁrmed, they become all the more entrenched and eventually lead to an excessive bias of expectations. In tightly connected systems, precise expectations are not able to incorporate complexity and unpredictability from the outside world. Luhmann’s point is that precision in expectations may actually make the system inaccurate5 because precision makes it more likely that expectations are not met, thus engendering disappointment, which may have dire consequences for the system. An autopoietic reading of the 3M example above might focus on the projection of expectations rather than the ingenuity of Fry or any inherent organizational characteristic. Fry’s expectations were precise, in the sense that only the right sort of glue would enable the realization of Post-it, and thus add novelty to the organization’s line of products. If his precise expectations had not been met (by chance in this case), the Post-it would probably not have been launched, at least not there and then. The question that poses itself is how the communication that frames expectations inﬂuences the ways in which organizations deal with novelty. We will assume that the distinguishing features of different functions within an organization are given by their modes of communication, and especially how they incorporate different degrees of redundancy in their communication. More precisely we consider the degree of redundancy in communication to inﬂuence the way in which novelty is engaged with. As a tentative metaphor, consider an adjustable beam of light from a torch. When set in a wide mode, the beam lights up more in the vicinity of the torch, its circumference is large, and it captures a higher number of objects than if it is set on ‘‘narrow.’’ It is less focused in the ‘‘broad’’ mode than in the ‘‘narrow’’ mode. In the narrow mode, on the other hand, objects can be seen that are further away from us, but at the expense of the number of objects that can be lit up in the vicinity of the torch. Let us assume that the torch was used to ﬁnd a speciﬁc object and not merely to light up around the holder of the torch. In a ‘‘broad’’ mode, the light would help us ﬁnd objects that we half expected to be there. They were close to us anyway, so ﬁnding them was not such a great surprise. In the ‘‘narrow’’ mode, however, the object we look for are further away, and the risk of not ﬁnding them is greater due to the narrower beam. On the other hand, ﬁnding them is associated with greater satisfaction because the search was more exclusive and focused. We will return to this example in the last part of the chapter.

5 Redundancy and Thematic Structures Broadly speaking the communication process relies on two factors for the reproduction and change in social systems: First, for any communicative system to uphold itself, it relies on redundancy of communication. Redundancy in this context means that the system produces more than it needs for its actual operation. This is 5

‘‘As a rule, the more explicit the expectation, the more insecure it is’’ (Luhmann, 1995, p. 308).

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necessary, because systems that produce for their actual needs only become extremely fragile in a changing environment. Thus, systems include in their operations the conditions for potentialities. Systems operate amid possibilities, and its communications contain references to possibilities which cannot all be pursued at the same time: There is always a core that is given and taken for granted which is surrounded by references to other possibilities that cannot be pursued at the same time. Meaning, then, is actuality surrounded by possibilities. The structure of meaning is the structure of this difference between actuality and potentiality. Meaning is the link between the actual and the possible; it is not one or the other. (Luhmann, 1990, p. 83)

Second, communication operates according to thematic structures (Luhmann, 1995).6 The production and recognition of difference is dependent on expectations, and expectations build up over successive time intervals of experience and reﬂection. Expectations, because they build up from successive experience over time, need a ‘‘core’’ of meaning which is sufﬁciently stable to form successive comparisons. Luhmann’s interpretation of expectations is that they lay the basis for learning, by enabling comparison between what happened and what was expected to happen. In this way, they provide the necessary elements for system behavior. Depending on how expectations are communicated within the system, they may lead to feelings of fulﬁllment or disappointment. Expectations with a high degree of precision, for example, what Luhmann refers to as claims, may in an unpredictable world lead to disappointment when they are not fulﬁlled. On the other hand, if fulﬁlled, they become all the more entrenched and eventually lead to an excessive bias of expectations. Bearing in mind that autopoietic social systems are based on communication, they depend on exhibiting a dominant discourse which provides consistency of meaning over time. For communication to make sense, it must be organized into ‘‘themes’’ in which ‘‘contributions’’ can be located (Luhmann, 1995, p. 155). Themes form context for contributions and form the basis for selection of contributions. Themes keep open the possibility of contributing, and as structure they make it possible for contributions to connect backwards in time, allowing recall of earlier contributions to a theme (Luhmann, 1995, p. 156). At the same time themes regulate who can contribute what. They may be exclusive in the sense that only a few may contribute, or they may allow for greater numbers to contribute their views. Organization researchers are familiar with themes being more or less open to different groups of participants. However, the degree of exclusivity of access has commonly been related to levels in a hierarchical sense. Whereas this makes sense in some cases, it unduly restricts understanding of communicative processes beyond a hierarchical view of organization. Redundancy in organizations has been an element in organizational analysis going back more than 40 years. Early writings in systems theory emphasized redundancy as a key characteristic of any system. Emery (1977), for example, suggested that 6

Themes allow communication to produce difference. Communication inevitably produces difference, one reason being that the object of the communication changes (the world moves on whether we like that or not; thus communication about something is like shooting at a moving target), another reason being that what is ‘‘out there’’ can only be guessed at; it lies there as a half-hidden possibility.

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redundancy has to be built into a system in order to achieve adaptability.7 Morgan (1986, p. 98), in his book Images of organization, sums it up thus: Any system with an ability to self-organize must have an element of redundancy: a form of excess capacity which, appropriately designed and used, creates room for manoeuvre. Without such capacity, a system has no real capacity to reﬂect on and question how it is operating, and hence to change its mode of functioning in constructive ways.

In organization theory, redundancy-related ideas have been pursued by writers, such as Weick and March, represented notably by their focus on loose versus tight coupling. In his analysis of educational systems, Weick suggests drawing a distinction between adaptation and adaptability, which relates closely to the view of innovation taken by Luhmann, who refers to innovation as structural change, and connectivity. Looseness of coupling, argues Weick, allows for adaptability, as it provides systems with the possibility to retain a greater number of mutations and novel solutions than would be the case with tightly coupled systems. Hence, argues Weick (1976, p. 7), ‘‘Loosely coupled systems may be elegant solutions to the problem that adaptation can preclude adaptability. It is conceivable that loosely coupled systems preserve more diversity in responding than do tightly coupled systems, and therefore can adapt to a considerably wider range of changes in the environment than would be true for tightly coupled systems.’’ March (1991), in discussing exploration and exploitation in organizational learning, also brings up the idea of loose coupling, suggesting that the reason so-called ‘‘garbage can’’ processes provide resistance in turbulent environments is that they provide a diversity advantage to the organization. A basis for Luhmann’s theory is that social systems depend on their own ability to reproduce themselves, but the system’s ability to reproduce itself lies not in repetition, but rather in connectivity. In an uncertain world, connectivity entails connecting to something that is yet to occur, and then cope with the consequences of that occurrence. In order to cope with the unexpected, the system develops redundancy of communication, which implies a surplus of informational possibilities and thus protects the system from the danger that something will be lost (Luhmann, 1995, p. 171). Seen in this way, redundancy may be interpreted as a surplus of connectivity enabled by communication.

6 An Example of Differentiated Themes, Redundancy, and Novelty If we think of organizations consisting of differentiated themes, each of which is open to its corresponding contributions, we come close to Luhmann’s idea of social systems as functionally differentiated. Functional differentiation is contrasted with 7

Early systems theory perspectives of organizations were concerned with questions such as how members of groups could maintain their respective roles in the group while being able to replace one another. Emery (1977) suggested that redundancy may be built into systems in basically two different ways. One type of redundancy, suggested Emery, is the redundancy of parts, meaning that if one part fails, another one takes over. Another type of redundancy, he suggested, is the redundancy of functions, meaning that the function may be fulﬁlled by different parts depending on the needs of the system.

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hierarchical and segmental differentiation. Themes are differentiated by their different modes of communication while remaining mutually autopoietic to one another (A˚kerstrøm Andersen, 2003). Breaking the organization down into functionally differentiated themes allows for exploration of how the organization may engage in novelty and thus avoid the pitfall or existing theory to assume that an organization as a whole is more or less innovative. First, it allows for exploring how different themes structure their communication, and hence how the structuring of communication allows for novelty. Second, it allows for studying how different organizational themes may interact in the search for novelty. To illustrate our point, we consider three typical themes in organizations: strategy, administration, and product development.8 In the theme of strategy, normally reserved senior management, organizations exhibit the most openness, which implies that at this level they should display the greatest adaptive ability. Strategic language contains words of considerable ambiguity, such as vision, market, leadership, etc. Ambiguity implies that words have a high redundancy of functions (Emery, 1977), in the sense that each term may contain several different meanings and thus several different courses of action. High redundancy in strategy makes sense, because strategy is supposed to encompass the organization in a world with surprises. Thus, strategic language with minimum redundancy would in itself make strategy ineffective as an organizational theme. Strategy represents the projection of an organization’s expectations, and not just expectations of individual functions. It is thus an integrative theme that interacts with other themes according to a logic of mutual autopoiesis; that is, the relationship is not to be seen as hierarchically differentiated, but functionally differentiated. What confers particularity upon strategy as compared to other themes within an organization is that it operates along two dimensions. On the one hand it operates between past and future, similar to March’s (1991) distinction between exploitation and exploration. Its integrative function forces strategy to bring forward the past into future projection on behalf of the organization. On the other hand it also performs the function of observing both the inside and the outside of the system deﬁned by organizational boundaries. In administration, a theme largely found in functions, such as accounting, ﬁnance, and personnel, organizations generally exhibit a low degree of openness to the environment and a correspondingly low degree of redundancy. Administration represents in many ways what Latour (1999) calls ‘‘centers of calculation.’’ In such functions, activity is based on ‘‘calculative devices’’ (Callon & Muniesa, 2005), which reﬂect the mode of communication that characterizes those functions. In ﬁnance departments, for example, communication is based on the language of numbers and models. In personnel departments, communication operates on the basis of judicial language, which is rooted in law (cf., A˚kerstrøm Andersen, 2003). Whereas strategy directs attention both to past and future, and to observation inside and outside the organization, and is thus expected to exhibit a reasonably high

8

These themes correspond in principle to Thompson’s levels, which he related to different degrees of openness.

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degree of redundancy of communication, administration would seem to exhibit the opposite characteristics. As the role is more controlling than exploring, administration is rooted in the past, which is projected onto an expected future. In this sense, administration functions as what Luhmann calls ‘‘historical machines’’ (Luhmann, 2000, p. 73). The role of administration is also primarily directed toward the inner workings of organizations. This concerns especially controlling functions, but does not apply to explicitly externally directed functions, such as marketing. However, in most organizations, marketing has a less controlling, and thus less determining, effect than do ﬁnance and personnel. Product development, the third theme, represents the ‘‘innovation’’ theme of organizations, although innovation is not exclusively related to products, but may also take place in relation to process, organization, or markets. Returning to the above example of the light beam, in a ‘‘broad mode,’’ the light beam, reﬂecting strategy, is based on language with considerable ambiguity, sheds light on a number of possible elements on the vicinity of the organization. A strategy beam, nevertheless, is directed outwards and inwards, at the past and toward possible futures. An ‘‘administrative beam,’’ on the other hand, is narrow, but directed inwards and largely toward existing arrangements created by the past. Its narrowness is reﬂected in the type of language that is used, such as laws and money. In Luhmann’s language that means that expectations are precise, and reﬂect claims, which may in an unpredictable world lead to disappointment when they are not fulﬁlled. On the other hand, if fulﬁlled, they become all the more entrenched and eventually lead to an excessive bias of expectations. This is, we suggest, a characteristic of administrative themes. Product development, however, is subject to what we would call ‘‘adjustable beams.’’ Organizations may choose whether to engage in broad exploration of opportunities in the technological area, or engage in narrow exploration of opportunities. Whether the search beam is set on broad or narrow, it is directed outwards and toward the future, which distinguishes communication in a product development theme from communication in an administrative theme. But the narrowness — the degree of redundancy in the communication of product development — has implications for how the organization engages with novelty. A low redundancy communication entails greater risk of failure because it projects more precise expectations. In the 3M example, Art Fry’s expectations were precise, because only a glue within a certain range of adhesion was acceptable. If that glue had not been found, Post-it would probably not have been invented by 3M, and may not have existed in that particular form today. Hence, low redundancy communication is risky, but may contain possibilities of high impact if realized in a market. High redundancy product development, on the other hand, is less risky. On the other hand, the chances of achieving impact as something truly novel may be lesser.

7 Conclusion We have focused on how organization seen as an autopoietic system, engages with novelty, being careful to avoid focus on how much difference/novelty is produced,

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which is the object of several works in innovation theory. Instead we have explored how novelty may be absorbed as a function of different degrees of redundancy. We have suggested that the degree of redundancy inﬂuences the degree of complexity and surprise that the system may absorb. A high degree of redundancy allows for unexpected outcomes to be absorbed and avoids disappointment. Increased redundancy enables a broader range of novelty to be absorbed by the organization. It may work as a ‘‘buffer’’ for themes in the organization that work with lower degrees of redundancy. Parsimony of communication, on the other hand, reﬂects a narrower range of redundancy, hence greater precision of expectations. Both these scenarios have their advantages and disadvantages. Greater redundancy allows for a broader range of possibilities to become actuality to the organization. We have further assumed that what distinguishes different functions within an organization is not so much what tasks they perform, but by what differentiates their modes of communication, and especially how different functions incorporate different degrees of redundancy in their communication. Using the examples of strategy, administration, and product development, we have argued that for the two former themes, their relative degree of redundancy is given by their functions, whereas product development exhibits an adjustable degree of redundancy which may be decided by the organization. We consider the degree of redundancy in communication to inﬂuence the way in which novelty is engaged. At the level of the individual theme, broadness comes at the expense of narrowness; hence, increased redundancy makes it more likely that only possibilities that lie in the vicinity of the day-to-day operations become of actuality. At the same time, extensive redundancy carries with it less risk, because expectations are less precise than what is the case with less redundancy. Conversely, narrowness entails greater risk of expectations not being met, and thus engendering greater disappointment when they are not met. However, when the organization is seen as a whole, it does not make sense to say that broadness comes at the expense of narrowness. On the contrary, it is quite possible that broadness (high redundancy) in one theme allow for narrowness (low redundancy) in another theme.

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PART IV APPLICATIONS OF AUTOPOIESIS IN ORGANIZATION PRACTICE

Chapter 10

Information in Organizations: Rethinking the Autopoietic Account Ian Beeson

1 The Theory of Autopoiesis I begin with a summary of the theory of autopoiesis, which is a condensed version of an account in an earlier paper (Beeson, 2001). That paper also presents an earlier version of part of the argument in the current chapter. Maturana and Varela’s (1980, 1987) theory of autopoiesis deﬁnes living systems as autopoietic (self-producing) systems. The theory gives a comprehensive and economic account of living beings from the simplest unicellular organisms (‘‘ﬁrst-order unities’’) to the most complex multi-cellular organisms (‘‘second-order unities’’). It extends, more tentatively, beyond organisms toward social systems (‘‘third-order unities’’). Social or business organizations are then examples of third-order unities. An autopoietic system exists as a network of relations and processes which continuously produce the components which realize that network as a concrete unity. For a class of unities of the same type (e.g., human beings), the organization of an autopoietic system can be described as a set of relations in abstract terms. The structure of an individual being — the components and relations that actually constitute a particular unity — is then one realization of that organization. The same organization is realized in different structures. Autopoietic systems are organizationally (or operationally) closed. The behavior of the system is not speciﬁed nor controlled by its environment but by its own structure, which speciﬁes how the system will behave under all circumstances. The system is structure determined. Systems, however, are not disconnected from their environments, but in fact in constant interaction with them, in an ongoing process that Maturana and Varela call ‘‘structural coupling’’ (1987, p. 75). System and

Autopoiesis in Organization Theory and Practice Advanced Series in Management, 185–199 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006011

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environment (which will include other systems) act as mutual sources of perturbation for one another. The changes in a living being which result from its interaction with its environment are triggered by a disturbing agent in the environment, not determined by it. It is always the structure of the disturbed system (the living being) that determines the changes. There can therefore be no ‘‘instructive interactions’’ by means of which something outside the system determines its behavior. It is the organizational closure of living systems that produces their autonomy and their individuality. Each individual has its own autonomous ontogeny (its own separate development and history), which is neither controlled by its environment nor determined by its class or species. This same closure dictates that there cannot be any inputs or outputs of a system including information) that, for the system, have independent, objective reality outside it. Over time, provided there are no destructive interactions between the system and the environment in which it realizes itself, the system will appear to an observer to adapt to its environment. What is in fact happening, though, is a process of ‘‘structural drift’’ occurring as the system responds to successive perturbations in the environment according to its structure at each moment. Though it might seem as if the system’s behavior is controlled by the environment, this is a feature of the observation, not of the operational reality. What in the generalization of their argument Maturana and Varela (1987, p. 181) call ‘‘third-order’’ couplings and unities arise as the natural result of the congruence between the respective ontogenic drifts of higher organisms of the same species. A third-order unity — a ‘‘social system’’ of animals or people — itself displays autopoietic organization to the extent that the network of participating individual organisms coproduce it through their reciprocal structural coupling. The reciprocal coordination mutually triggered among the members of a social unity, they call communication (1987, p. 193). So deﬁned, communication is an aspect of social behavior, emerging out of structural coupling and drift in social groups, and not a separate or distinct mechanism suddenly appearing in evolutionary development. Communication is then the basic behavior from which language (or ‘‘languaging’’) emerges. Human linguistic behavior is a domain of reciprocal ontogenic structural coupling which human beings establish and maintain together. Although from an external perspective, an observer may be able to describe words as designators of objects or situations in the world, the operational reality of our use of language with one another reﬂects a structural coupling in which words are ‘‘ontogenically established coordinations of behavior’’ (Maturana & Varela, 1987, p. 208). Just as Maturana and Varela put communication before language, so they suggest that mind and consciousness, far from being prior to language, arise out of it. As they phrase it, ‘‘language is a condition sine qua non for the experience of what we call mind’’ (1987, p. 231). Reversing the Cartesian cogito ergo sum, they place thinking, in the sense of conscious thought, at the end of a chain which starts with autopoiesis, then proceeds through structural coupling, communication, and language. By putting being before thinking, the theory of autopoiesis offers an approach to communication that has existential and ontological, rather than epistemological foundations.

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2 Difﬁculties with Information A disturbing consequence of the theory of autopoiesis for information scientists and theorists, for systems thinkers, and more generally for organization theorists is the rejection of the notion of information and related notions of message and communication, representation, input and output, feedback, and control and regulation. These are at the heart of the systems model of organization, central to organization theory in general, and key concepts in computing and engineering science. Maturana and Varela (1980, p. 90) write: ‘‘Notions such as coding and transmission of information do not enter in the realization of a concrete autopoietic system because they do not refer to actual processes in it. The notion of coding is a cognitive notion which represents the interactions of the observer, not a phenomenon in the observed domain. The same applies to the notion of regulation.’’ They reject the idea that the organism, via its nervous system, builds an internal representation of the world outside (1980, p. 129). While the common view is that an organism inputs information from the environment that it then uses to build a representation of the world; they argue that organism and nervous system operate with structural determination, which means that the structure of the environment can only trigger changes in the organism, not specify them. It may look to an observer as if the organism’s behavior arises from an internal representation of the environment, but in fact it is the structural state of the nervous system at each moment that determines how any changes in the environment can perturb it. The idea of the nervous system picking up information from the environment and modeling a world from it and the idea of the brain as an information-processing device are misconceptions, only useful for talking among ourselves about how the brain and nervous system appear to work, and no kind of scientiﬁc explanation of how in fact they do work. And they reject the idea of information being transmitted in communication (1980, p. 196). Communication, according to them, occurs whenever there is behavioral coordination in a situation of structural coupling. But this is not the same as information passing along a pipe and determining behavior at the far end. As they point out in ordinary language, saying does not automatically produce listening. Communication is to be understood not in terms of transmitted information, but in terms of what happens to the person who hears. There can be, as they say, no instructive interactions. In his preface to Maturana and Varela’s ﬁrst book (1980, p. 69), Stafford Beer, prominent cybernetician and proponent of the idea of the viable system, acknowledges that he had previously thought that ‘‘the whole story’’ of the viable system was grounded in information, codes, messages, and mappings. But he is persuaded by the argument of autopoiesis, and concludes that the authors must be right: ‘‘Nature is not about codes; we observers invent the codes in order to codify what nature is about.’’ The theory of autopoiesis thus poses immediate challenges to organization theory. Core concepts of organizational science, and of systems theory, are rejected or transformed. The idea that organizational systems are driven by models or representations, and by the transmission of information, is challenged, as is the idea that behavior can be controlled through instructive interaction. These are

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constructions by an observer, who is explaining a sequence of actions from an external perspective. The effective operational reality is one of autonomous individuals engaging in structure-determined behavior, coupling, and drift. However, there does remain a question of whether ‘‘third-order unities,’’ including organizational systems, are in fact autopoietic systems. The generalization of Maturana and Varela’s theory from biological to social systems is problematic (for them as well as others). An observer may see a third-order unity as autopoietic on the grounds that its realization depends on the autopoiesis of the (second-order) unities which integrate it. But it could be argued that, unless the third-order system is itself deﬁned by relations between components which in turn produce the relations, it is not truly an autopoietic system. To see an organization as a third-order unity, we would have to understand it as in some sense producing and maintaining its own components (which include human beings) and the relations between them. Two ways then present themselves for trying to deal with the problems that the theory of autopoiesis poses for organizational theory in relation to information, representation, communication, and related concepts. One is to try to recast the notion of information in a different way, which accepts the criticism from autopoiesis that information cannot have an objective external reality for organisms which are structure determined, and develops instead an idea of information as something which may trigger (but not determine) a response. The other is to escape or ward off the strictures of autopoietic theory by denying that it applies to organizations in a precise sense, or by reorienting the theory to deal with higher level systems directly, instead of with organisms. If organizations are not autopoietic in the same sense as organisms, it could be that information and communication can assume an objective role in organizations even if they cannot for organisms. A third way of handling the autopoietic analysis of organizations is to treat it as a metaphorical or imaginative account, rather than an empirical or scientiﬁc one. The rest of this chapter will be devoted mainly to the ﬁrst of these approaches, starting in the next section. A sketch of the other two approaches will be given in the remainder of the present section. Kickert (1993) is one author who sees autopoiesis as useful as source of ideas for stimulating thinking about organizations and opening up new possibilities of action. Reviewing the relevance of the theory of autopoiesis in the ﬁeld of public administration, he concludes: ‘‘y The possibilities of a strict conversion of the autopoiesis model into a valid model that can be used in the administrative sciences are limited. The usefulness of the model does not seem to lie in strict adherence to the original and literal translation, but rather in its power as a source of creative lateral thinking.’’ Morgan (1997, Chapter 8), in his well-known volume on organizational metaphors, discusses autopoiesis under the broader heading of ﬂux and transformation. He notes three main features of Maturana and Varela’s theory of living systems: autonomy, circularity, and self-reference. He suggests that organizations, from an autopoietic perspective, can be seen as attempting to turn their environments into extensions of their own identity. This implies a focus on establishing and projecting the organization’s identity and on shaping relations with the environment in the organization’s favor. He thinks this could produce narcissistic, egocentric, aggressive

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organizations. While such a characterization of an organization might resonate with some members, it also seems to be a misreading of autopoietic theory. Operational closure precludes an organization’s taking over its environment (or vice versa). Autopoiesis suggests a subtler analysis in terms of structural coupling. The danger of taking such ideas as autopoiesis metaphorically is that they may be taken on in a cosmetic or superﬁcial fashion. Autopoiesis could, e.g., be promoted as a doctrine of self-aggrandizement or survival of the ﬁttest, or used to explain why organizational learning can never work. But this would be to distort the original theory. More positively, a loose or informal application of autopoietic ideas might help us understand why patterns of organizational behavior sometimes seem to reproduce themselves in a manner which is conservative and hard to change, or why individuals and groups can be resilient sometimes in adverse circumstances. We can call on ideas from autopoiesis to support actions in favor of individual autonomy or of a gradualist approach to change. Luhmann’s development of the theory of autopoiesis in Social Systems (1995) is not a metaphorical account but a thorough and ambitious reworking of the theory. He reshapes the central ideas of autopoiesis to produce a new focus on social systems as continuous self-referential productions. He drops the premise that social systems are living systems, so severing social systems from their biological foundations. Faced with the question of how social systems can be said to produce the people that compose them, Luhmann’s answer is that the basic elements of social systems are not people but communications. Communication is no longer seen, as in Maturana and Varela, as an aspect of behavioral coordination, but as the central subject matter. The focus on living or being is replaced by a focus on meaning and its achievement. Although he describes consciousness and the formation of social systems as evolutionarily co-emergent, Luhmann separates psychic from social systems, in order to establish the autonomy of the latter. He is thereby able to reject the idea of the social as derived from a realm of intersubjectivity, as well as the idea of communication as an interaction between subjects or a transmission between consciousnesses, and can concentrate instead on the social system as an autonomous (organizationally closed) system of communications. Luhmann’s theory, and in particular his analysis of communication as a synthesis of information, utterance, and understanding, restores information to a central place in organizational theory, but at the cost of deleting the human subject. The existential and ontological aspects of the original autopoietic account of living organisms are left undeveloped as Luhmann opts for a thoroughly epistemological treatment of social systems. An extensive new theory of social systems with its roots in autopoiesis is delivered, but the founding idea of the self-producing organism is not at the center of it. Information returns to the center of the stage, and since it has been theoretically separated from the beings that use it, there is no urgency to consider whether the original rejection of information in autopoietic theory has any substance to it. Luhmann’s autopoietic theory looks better aligned with mainstream organizational and systems thinking than does the biological version. His approach ﬁts well with theories of organization which focus on rules, ﬂows, and structures more than on individual consciousness or cooperative action. Luhmann’s theory is radical in its

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rewriting of sociology and of autopoiesis, but, as an abstract systems theory, is less foreign to organizational theorists than Maturana and Varela’s precursor. Both the metaphorical and social systems approaches to autopoiesis outlined here, in different ways and for different reasons, avoid confronting the conceptual difﬁculties surrounding the notion of information in the original theory. In the following section, we will return to the idea of information, especially in the organizational context, and try to develop an understanding of information which meets the reservations expressed in the original biological account of autopoiesis. The standard message model of communication or information will be presented ﬁrst, and then critiqued from the perspective of pragmatics. Pragmatics offers a different angle from autopoiesis, but it will be seen that the reservations about the standard model of information in the two perspectives are compatible.

3 The Message Model of Communication Figure 1 (derived from Figure 1 in Shannon & Weaver, 1949, p. 34) shows in schematic form the model of communication as a transmission of signals, which is the basis of the communication theory (or information theory) derived from Shannon’s work on channel capacity and noise. The source generates a message (which consists of information) for communication to a (remote) destination; the message is operated upon (‘‘encoded’’) by a transmitter in such a way as to make it suitable for transmission (as a signal) across a medium (channel). The signal is subject to distortion by noise in the environment, so that the received signal is in general not identical to the transmitted signal. The receiver operates upon the incoming signal so as to ‘‘decode’’ it, if possible removing the errors introduced by noise and restoring the original message for the destination. Shannon’s theories rely crucially on being able to measure the variety in the set of messages produced by the information source. This quantity — the entropy of the source — is calculated from the relative probabilities of selection of each of the possible constituents of a message: ‘‘We can think of a discrete source as generating the message, symbol by symbol. It will choose successive symbols according to certain probabilities depending in general on preceding choices as well as the particular symbols in question’’ (Shannon & Weaver, 1949, pp. 39–40). This kind of process, in which a discrete sequence of symbols from a ﬁnite set is produced according to a set of probabilities, is called a stochastic process. Shannon was able to show that, for such a discrete source, an efﬁcient encoding scheme could be devised which achieved maximal utilization of the channel. His fundamental result for a noiseless

information source

message

encoder/ transmitter

signal

noise

Figure 1: Communication as signal transmission.

receiver/ decoder

message

information destination

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channel was that a channel capable of transmitting C bits per second could transmit symbols from a source with an entropy of H bits per symbol at an average rate no higher than C/H symbols per second. He also showed that, for any channel whose capacity was greater than the noise it was subjected to, an encoding scheme could be devised which would guarantee accurate delivery of messages. Shannon’s theories lie at the foundation of communication engineering. He and his interpreter, Weaver, were at pains to point out that a specialized notion of ‘‘information’’ was being used here, related not to any intention to convey meaning, but rather to variety in the message set and uncertainty in the source. Weaver postulated three levels of communication:

Level A. How accurately can the symbols of communication be transmitted? (The technical problem.) Level B. How precisely do the transmitted symbols convey the desired meaning? (The semantic problem.) Level C. How effectively does the received meaning affect conduct in the desired way? (The effectiveness problem.) (Shannon & Weaver, 1949, p. 4)

Weaver acknowledged that Shannon’s theories applied only to Level A, but then went on to claim that ‘‘the theory of Level A is, at least to a signiﬁcant degree, also a theory of Levels B and C’’ (Shannon & Weaver, 1949, p. 6). Ritchie (1991) believes this generalization of Weaver’s to be unfounded, and to be based at least in part on a confusion between two meanings of uncertainty: the statistical uncertainty relating to the set of symbols which can be generated by the source and the subjective or cognitive uncertainty of an observer about what particular message was sent in a noisy channel (pp. 53–55). Weaver’s treatment of Levels B and C is in fact quite vague, and verges on the mystical. He wants the theory to be generalizable: ‘‘y the mathematical theory is exceedingly general in scope, fundamental in the problems it treats, and of classic simplicity and power in the results it reaches. y [T]he theory is sufﬁciently imaginatively motivated so that it is dealing with the real inner core of the communication problem — with those basic relationships which hold in general, no matter what special form the actual case may take’’ (Shannon & Weaver, 1949, p. 25). And he is willing to restrict language behavior to make the theory ﬁt: This idea that a communication system ought to try to deal with all possible messages, and that the intelligent way to try is to base design on the statistical character of the source, is surely not without signiﬁcance for communication in general. Language must be designed (or developed) with a view to the totality of things that man may wish to say; but not being able to accomplish everything, it too should do as well as possible as often as possible. That is to say, it too should deal with its task statistically. (p. 27)

Misinterpretation of Shannon’s theories thus appears to date back to the very beginning. The model of communication as signal transmission seems appealing and powerful, and remains inﬂuential. It seems attractive to extend the model from messages sent between devices so that it equally covers a conversational exchange between human speakers. It may be tempting to follow Weaver’s suggestion and keep

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the basic technical model in all its essentials — simply adding levels of semantic and pragmatic source/destination, encoding/decoding, transmission/receipt and noise, and assuming that analogous mechanisms are at work at each level. But, in fact, the generalization of the technical model to semantic and pragmatic levels must be contested, since it appears beset with ﬂaws. Linguistic communication between people is not the same as, and not very like, signal transmission between machines. It is not a stochastic process. The Shannon model persists in organizational theory and in management thinking even though it does not give an adequate representation of human communication. Shannon devised the model for signal transmission, but it has appeared to his interpreters to be capable of more general application. Because this possibility of generalization also seems to accord rather strongly with ideas about the detachability of information from context and the desirability of controlled communication, Shannon’s model has not been deﬁnitively curtailed. This is no doubt partly a political matter, but there are also theoretical and technical issues surrounding attempts to store information in databases and transmit it across networks.

4 Pragmatics and Relevance Akmajian, Demers, and Harnish (1998, p. 65) list four problems with what they call the Message-Model (the Shannon model generalized to talk exchanges): 1. 2. 3. 4.

it identiﬁes the message to be sent with the literal meaning of the words uttered; it depicts the process of encoding the meaning into sounds as linear and sequential; it depicts the decoding process from sounds into meaning as linear and sequential; it depicts the hearer’s recovery of the intended message as being identical with the decoding of the meaning of the sentence.

Problems 2 and 3 relate to perceptual and semantic aspects of communication, while 1 and 4 are more concerned with the intentions and uses in communication — i.e., pragmatic aspects. Akmajian et al. show that the production and perception of utterances is not linear and sequential. Even at the phonological level, speakers do not simply produce, nor hearers perceive, a discrete string of phonemes. Rather, whole words, phrases, and sentences are produced and perceived, in which successive sounds color one another. (To give one of their examples, the sequence -str- in construe is articulated differently from the same sequence in constrict, because of the following vowel.) From the hearer’s side, actual speech is experienced as a physically continuous stream of sound, from which the hearer derives individual sounds, words and sentences, not by a sequential process of signal extraction but by hearing the larger utterance as a whole. One hears what is said only by virtue of knowing the language and understanding the context of communication. At the grammatical level, sentences with the same surface structure

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may have different deep structures. Akmajian et al. give the example (p. 70): ‘‘I saw the man with the telescope’’; that two different meanings are available (to both speaker and hearer) shows that the meaning of a sentence cannot in general be derived sequentially from the meaning of the individual words. Proceeding to the pragmatic level (roughly equivalent to Weaver’s Level C — ‘‘effectiveness’’), Akmajian et al. list six inadequacies of the Message-Model (pp. 75–78): 1. Expressions can be ambiguous. A hearer tries to resolve ambiguity by assuming the speaker’s remarks to be appropriate to the context. 2. Messages can contain references to people, events, or things not uniquely determined in the message itself. (Pronouns are an obvious example, but many expressions contain imprecise references.) 3. The Message-Model omits any account of the speaker’s communicative intention, or the hearer’s recognition of it. 4. People often speak non-literally; they do not mean what their words mean. Irony, sarcasm, and metaphor are common examples. 5. People often speak indirectly; they mean something beyond what their words mean. For instance, ‘‘My car has a ﬂat tire’’ may be given as a request for a repair when said to a mechanic, or as a reason for illegal parking when said to a policeman. 6. Communication may not always be the reason for speaking. We may simply be declaring something (that someone is married, ﬁred, or innocent, for instance), or trying to impress, or deceive, or persuade. These acts can succeed whether communicative intentions are recognized or not. Akmajian et al. conclude their analysis of the Message-Model by declaring it inadequate to account for the full richness of normal human language use. They also suggest that for a hearer to identify a speaker’s communicative intentions, more than just a common language is required: ‘‘A shared system of beliefs and inferences must be operating, which function in effect as communicative strategies’’ (p. 78). They propose a model of their own to show how a hearer identiﬁes a speaker’s communicative intention (pp. 78–79). In this, the hearer must identify what the speaker has uttered, identify what that must be intended to mean in this context, and resolve any references. The hearer must then decide whether the utterance is to be taken literally or non-literally, and must ﬁnally identify if any indirect communication is intended. Communication is only complete when the hearer has accomplished all these steps. One could go further and suggest that communication can never be complete (even though it can be ﬁnished). Merleau-Ponty (1992, p. 390) comments that language always outruns us that there is always a surplus or excess of meaning beyond what has been said or heard. The analysis by Akmajian et al. shows that, as a basis for spoken communication between people, the Message-Model, derived from Shannon’s model of communication, is seriously ﬂawed. Speaker and hearer in a human talk exchange do not operate

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as stochastic processes. They are not engaged in selecting symbols from a ﬁnite set of symbols on a basis of probability. Successful communication depends on a good deal more than the transmission and receipt of symbols or sounds — for instance, common language, shared context, resolution of references, resolution of ambiguities, identiﬁcation and interpretation of non-literal meanings, communication of intention, and identiﬁcation of indirect meanings. Sperber and Wilson (1986, 1998) have further developed the pragmatic analysis of communication by building on Grice’s theory of implicature (Grice, 1975). Grice draws a critical distinction between what is said by a speaker and what is implied. What is said is closely related to the conventional meaning of the words uttered, and can be worked out from an understanding of the language. What is implied is sometimes also derivable from the words uttered and their conventional meanings, but more generally, in a conversation, implications derive not from the words, but rather from the working out of understandings between the participants in the course of their exchanges. He pictures conversation as a cooperative venture. Sperber and Wilson give an account of inferential communication derived from Grice’s, but founded on the central notion that to communicate is to claim someone’s intention, and hence to imply that the information communicated is relevant (Sperber & Wilson, 1998, p. 82). Communication is achieved when a speaker produces ostensive stimuli which provide evidence to the audience from which the speaker’s informative intention can be inferred (p. 91). They proceed toward a deﬁnition of relevance in which — other things being equal — an assumption with greater contextual effects is more relevant than one with smaller effects, and an assumption requiring a smaller processing effort is more relevant than one requiring a larger effort (p. 96). The principle of relevance is simply this: that a hearer treats it as axiomatic that the speaker has done his best to be maximally relevant (p. 361). In interpreting an utterance the hearer uses this principle as a guide, on the one hand towards correct disambiguation and assignment of reference, and on the other in deciding whether additional premises are needed [i.e., for indirect implications], and if so, what they are, or whether a ﬁgurative interpretation was intended. (Wilson & Sperber, 1998, p. 361)

Sperber and Wilson do not propose that their version of an inferential model of communication should be elevated into a general theory of communication (p. 91). They even leave a limited role for the message model (which they call the ‘‘code model’’): they suggest that a hearer recovers the linguistic meaning of an utterance by a form of decoding, but then applies inferential processes to recognize the speaker’s intentions (p. 82). This pragmatic analysis of communication goes a long way toward defusing the arguments leveled against information in the theory of autopoiesis. In this analysis, information is no longer seen (as in the message model) as existing objectively and externally to those using it. The emphasis instead, as Maturana and Varela would wish, is on how a hearer responds to a communication, and how members of a community use language to coordinate their behavior.

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5 Autopoiesis and Ontology Dell (1985), in his illuminating comparison of the work of Maturana and Bateson, remarks that ontology is ‘‘the road not taken’’ in Bateson’s thinking and suggests that the biological ontology delineated in Maturana’s work could provide a sound foundation for the social and behavioral sciences. As commented earlier, Luhmann’s signiﬁcant reworking of autopoiesis followed an epistemological rather than an ontological route. Ontology is the branch of philosophy concerned with what things exist in the world. In Maturana and Varela’s theory, the most fundamental behavior (comments Dell) is to exist, and the most fundamental knowledge is to know how to exist. In autopoietic theory, the world is populated by (structure-determined) living organisms. Dell explains the signiﬁcance of Maturana’s ontology thus: Structure-determined living systems automatically become organized into interactional systems. Whenever two or more structurally plastic living systems interact they will begin to co-evolve a closed pattern of interaction. They will form a system. When a system is understood in terms of structural coupling, it can be seen that there is no need to explain the system’s organization in terms of homeostasis, systemic rules, or control hierarchies. y The system arises naturally from the way its structurally plastic components ﬁt together. Such a system results from, and is, the structural coupling of its components. (Dell, 1985, pp. 13–14)

The theory of autopoiesis, in its original form, is fundamentally ontological in character. It is concerned with the existence of living organisms, how structuredetermined unities continue to exist by producing themselves, and how they interact with one another. The emphasis is on what it is to exist, and to live, and what it is to be engaged in interaction (as distinct from observing interaction). When this perspective is applied to information, and its use in organizations, the focus is not on the information as such, existing independently, perhaps stored in a document in a database, but on the exchange of information in interaction. This was also the emphasis in the pragmatic analysis of information and communication presented in the previous section. The critical point is that, in the interaction between two parties, each party is producing itself; it is inﬂuencing, but not producing, the other. There are no instructive interactions, and there are no ﬁxed or ﬁnal meanings in any of the messages. Gibson’s theory of information pickup (1986, Chapter 14) appears at ﬁrst glance to have a contradictory view of information to that in the theory of autopoiesis, because it maintains that information is present in the world, and is picked up by organisms. But his notion of information is more fundamental and more radical than that attacked by Maturana and Varela or assumed in our previous discussion of communication and relevance. In his theory, individuals acquire information not from communication or instruction, but from perceiving the world: y picking up information is not to be thought as a case of communicating. The world does not speak to the observer. y Words and pictures convey information, carry it, or transmit it, but the information in the sea of energy around each of us, luminous or mechanical or chemical energy, is not conveyed. It is simply there. The assumption that information can be transmitted and the assumption that it can be stored are appropriate for the [mathematical] theory of communication, not for the theory of perception. (Gibson, 1986, p. 242)

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In Gibson’s theory, information is picked up through ‘‘perceptual systems’’ (not the senses as ordinarily understood) from the environment. He is concerned, not with the perception of forms, colors, time, motion, or other abstractions in a laboratory setting, but with the perceiver’s experiencing of places, events, substances, and objects in an eventful world. The perceiver is also aware of being in the world. Gibson portrays perception as a psychosomatic act. A perceptual system is active. It consists of organs located in a moving body, not receptors. A perceptual system does not merely receive stimuli, but obtains information, actively. We live in a sea of energy, and experience it not as a sequence, but as a ﬂux. Perception involves the concurrent registering, not only of self as well as of environment, but also of change as well as persistence in that ﬂux. Information is always available, and individuals are always actively seeking it: A perceiver can keep on noticing facts about the world she lives in to the end of her life without ever reaching a limit. There is no threshold for information comparable to a stimulus threshold. Information is not lost to the environment when gained by the individual; it is not conserved like energy. (p. 243)

Gibson’s theory of information and information pickup recasts information ontologically, relocating information in the world and re-presenting the world to us as the information we perceive, through living in it. It offers an approach to information compatible, I suggest, with the theory of autopoiesis, and indeed seems capable of providing an explanation of the notions of plasticity and structural drift central to the autopoietic account. The idea of organizational closure clearly does not mean that organisms are condemned to repeating a ﬁxed behavioral repertoire, since they are able to change as they interact with their environment. Information pickup, as an active engagement with the world, must be part of behavioral variation in the organism.

6 Information in Organizations The theory of autopoiesis can be combined with the inferential model of communication and the theory of information pickup to give an approach to information in organizations that emphasizes action, interaction, and attention rather than storage and transmission. In this approach, human beings are regarded as active and attentive in their pursuit of information. Information is obtained by them continuously as they move through their environment or as they interpret the implications and resonances of what is communicated to them. They are constantly interacting with one another, always on the lookout for information, and always oriented toward working out its relevance. The lacunae and ambiguities always present in the exchange of messages in organizational settings create the conditions in which information problems are bound to occur. The expansion of information systems across organizational boundaries, because it attenuates the connections between communicating partners

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still further, will inevitably make matters worse. The solution cannot be found in formal information or computer systems, however far extended, and however much enhanced by the introduction of intelligent agents and similar software assistants to track and prompt our data usage. Such ‘‘active’’ information systems will help people marshal their data, but the derivation of meaning will perforce remain a human prerogative. In the familiar designs and expectations of current information systems, there is no assumption that (nor any attempt to make) information always available, or always relevant. Information is rather — under a rationale of efﬁciency or control — conﬁned and limited within reporting cycles, hierarchical regimes of disclosure, and predeﬁned accesses or queries. The question of relevance does not come up for two reasons: the meaning and implication of any item is presumed to be fully contained within the item itself (which in turn assumes a common context for originator and recipient of the information) and whatever interpretation is made should in any case be the standard one laid down in operating procedures. The ability of individuals to ﬁnd new information and make new interpretations is thus obstructed, often on purpose. The expressive range of language is severely curtailed by conﬁning information within prearranged formats and conventional meanings; the entire ﬁgurative dimension, for instance, is sacriﬁced in the reduction of information to records and recipes. Information gets lost as interaction and coupling are blocked in the mire of procedure and hierarchy. Were managers and designers to recognize that individuals naturally and inevitably interpret information and seek relevance in it (however obscure it is made), they could then design systems which exploited this capability and encouraged interpretation. For example, structured information sources could be expanded (either within the system or in supplementary documentation) to include explanations or commentaries on data items and algorithms or decision routines. This would recognize and exploit the fact that individuals are predisposed to try to make sense of the information that confronts them. If, further, the author or agent currently responsible for each data item and routine were to be made known to users, the possibility would be opened of a conversation between originator and user which could deepen relevance, refocus attention, and perhaps suggest different or additional uses of information. Taking up Sperber and Wilson’s suggestion that relevance can be plotted on a continuum, we can imagine an information analysis of an organization which concentrated less on the formal structure of items, and more on the contextual effects and interpretive effort associated with each item. Sperber and Wilson have commented that a full account of how people understand one another’s utterances — especially, but not only, ﬁgurative ones — requires a theory of rhetoric, which is to say a theory of argument and persuasion. If we are to make sense of and more effectively use the information abundantly available in our organizations, we need to be able to judge what is relevant, to make convincing interpretations, and to persuade others of its import. This is so because meaning is not to be found in the symbols, and not in the databases nor the diagrams, but is constructed by us as we engage with the world and in the spaces of conversation which stretch between us.

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7 Conclusion The rejection of the notion of information, as ordinarily understood, in the theory of autopoiesis, presents problems to theories of organization rooted in ideas of information, control, system, and communication. But the rejection seems well founded if the idea of a self-producing organism is taken seriously. There are various ways of trying to resolve the issue. We can say that autopoiesis cannot be extended to ‘‘third-order unities’’ (societies and organizations), so that its strictures are irrelevant, even if the autopoietic account can be useful metaphorically. Luhmann’s solution is to work out a full-blown autopoietic account of social systems in which the selfproducing entities are not individual human beings but communications. This is elegant, but discards autopoiesis’s biological foundation and so breaks the connection with living in the world. A reworking of ideas of information and communication using theories from pragmatics takes us closer to the autopoietic view by switching focus from stored information and instructive interaction toward a cooperative search for meaning and relevance. Such an approach, however, remains epistemologically focused (on how knowledge is exchanged), while (as Dell suggests) autopoiesis is oriented ontologically (toward existing and what exists). A more radical reworking of the idea of information taken from Gibson’s perceptual theory has therefore been suggested as more compatible with autopoiesis. In terms of the design and interpretation of organizations and organizational systems, an autopoietic account, coupled with a pragmatic approach to communication and a Gibsonian treatment of information pickup, would shift focus (and effort) away from information storage, control, and abstraction toward richer forms of interaction and awareness. While the message of organizational closure from autopoiesis has been taken to mean that individuals and organizations have limited capacity for change (so perhaps need to be forced), the positive conclusion from the theory is that individuals and organizations are autonomous, not ﬁnally determinable nor controllable, and so are open, even within their structural constraints, to inexhaustible possibilities.

References Akmajian, A., Demers, R. A., & Harnish, R. M. (1998). Overcoming inadequacies in the message-model of linguistic communication. In: Kasher, A. (Ed.), Pragmatics: Critical concepts (Ch. 78, Vol. V, pp. 63–81). Beeson, I. A. (2001). Implications of the theory of autopoiesis for the discipline and practice of information systems. In: N. L. Russo, B. Fitzgerald & J. I. DeGross (Eds), Realigning research and practice in information systems development (pp. 317–332). Norwell, MA/ Dordrecht: Kluwer/IFIP. Dell, P. F. (1985). Understanding Bateson and Maturana: Toward a biological foundation for the social sciences. Journal of Marital and Family Therapy, 11(1), 1–20. Gibson, J. J. (1986). The ecological approach to visual perception. Hillsdale, NJ: Lawrence Erlbaum Associates. Grice, H. P. (1975). Logic and conversation. In: Kasher, A. (Ed.), Pragmatics: Critical concepts (Ch. 54, Vol. IV, pp. 145–161).

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Kickert, W. J. M. (1993). Autopoiesis and the science of (public) administration: Essence, sense and nonsense. Organization Studies, 14(2), 261–278. Luhmann, N. (1995). Social systems. Palo Alto, CA: Stanford University Press. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht: D. Reidel. Maturana, H. R., & Varela, F. J. (1987). The tree of knowledge. Boston, MA: Shambhala. Merleau-Ponty, M. (1992). Phenomenology of perception. London: Routledge. Morgan, G. (1997). Images of organization. Thousand Oaks, CA: Sage Publications. Ritchie, L. D. (1991). Communications concepts 2: Information. Newbury Park, CA: Sage. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. UrbanaChampaign, IL: University of Illinois Press. Sperber, D., & Wilson, D. (1986). Relevance: Communication and cognition. Oxford: Blackwell. Sperber, D., & Wilson, D. (1998). Pre´cis of relevance: Communication and cognition. In: Kasher, A. (Ed.), Pragmatics: Critical concepts (Ch. 79, Vol. V, pp. 82–115). Wilson, D., & Sperber, D. (1998). On Grice’s theory of conversation. In: Kasher, A. (Ed.), Pragmatics: Critical concepts (Ch. 63, Vol. IV, pp. 369–382).

Chapter 11

Autopoiesis and the Evolution of Information Systems Marleen Huysman, Heico van der Blonk and Edu Spoor

1 Introduction The relation between organizational change and information systems has received much attention in the information systems literature. Much of this research has concentrated on the effects of organizational change on information systems and vice versa. Such research has generated rich insights in the facilitating as well as constraining role of information systems in the process of organizational change. Many analyses assume a distinction between the system and its environment, the organization. Information systems are seen as being relatively stable entities while the environment is a source of change and uncertainty. Such a perspective stresses the need for a continuous adaptation of the information system to its dynamic environment. In this paper we challenge this conception of the relation between information systems and organizational change by outlining a perspective that focuses on the process of evolution itself that leads to the effects commonly researched in the information systems literature. This reﬂects a wider interest within the study of information systems and organizations that calls for a richer understanding of the generative mechanisms through which information systems and organizations evolve. Evolution, in this view, is not conceived as a trajectory of improvement, which leads to a desired end-result through several phases but as a process of which we need to explore its inner workings. We then might develop a richer insight as to how and why information systems and organizations change and stabilize, irrespective of the effects they produce. In this paper we draw on the theory of autopoiesis, a recent biological theory which sheds new light on the evolution of living systems and which might be relevant for the evolution of information systems as well. In this theory the common relation between a system and its environment is blurred. The environment only exists through Autopoiesis in Organization Theory and Practice Advanced Series in Management, 201–213 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006012

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perception, and thus is part of the system. Change, in this view, is generated internally and evolution is not a process of adaptation but of maintaining the system’s selfidentity. We attempt to apply the ideas of autopoiesis metaphorically to the evolution of information systems, which we illustrate with an empirical study, and we present some theoretical implications of this perspective. The next section presents a short overview of autopoiesis with respect to the issues that are relevant for our purposes. In the third section we will outline how we use autopoiesis for the analysis of the evolution of information systems. The ideas that are metaphorically derived from this theory are illustrated in the fourth section by drawing on a case study of the evolution of an information system over 13 years. Subsequently we analyze the case by drawing on autopoiesis and discuss our ﬁndings in the light of contemporary social theory. In the sixth section we end the paper with the conclusion that autopoiesis opens new and interesting lines of thinking about change in and of information systems.

2 The Theory of Autopoiesis Maturana and Varela have formulated the theory of autopoiesis in the early 1970s as an explanation for the nature of living systems (Maturana & Varela, 1980). The term autopoiesis is adopted from Greek and means self-production. The theory is a new approach to systems thinking. The central idea of autopoiesis is that living systems produce themselves. The system’s components and processes jointly produce the same components and processes, thus establishing an autonomous, self-producing entity (Mingers, 1995). Autonomy of a system is the key feature of living beings and refers to the ability to specify what is proper to it (Maturana & Varela, 1992). The mechanism that makes living systems autonomous is autopoiesis. The recognition of the autonomy of a living system implies that the traditional distinction between a system and its environment is no longer valid because an external observer makes such distinctions. Instead, autopoiesis poses that a living system continuously constitutes its own boundaries, perceives its surroundings (which Maturana and Varela call the medium) in its own ways, thereby constructing an environment. In Varela’s words: ‘‘[W]e are becoming more and more interested in an epistemology which is not concerned with the world-as-picture, but with the laying down of a world’’ (Varela, 1984). In explaining the workings of the human brain, for example, Maturana and Varela say that the brain produces images of reality which are determined by how the brains themselves are structured. In other words, the patterning of the brain determines the perception of the world. With those images, interaction occurs that may lead to changes in the organization of the brain, depending on the actual experience. In this sense, the environment is not ‘‘something out there’’ but it is actively constructed by the system itself as part of its own organization. Hence, the environment needs to be seen as part of the system. Although a living system operates in a physical environment, the relation to that environment and the interaction with it is determined internally. Thus, for example,

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certain berries are poisonous for human beings. This is, however, not the intrinsic property of the berries but dependent on the physical properties, i.e., the organization, of the human being. For other living systems, certain birds for instance, the berries may not be poisonous at all. In maintaining autopoiesis the identity of a living system is of central importance, and all activity is meant to preserve this identity. All interaction which the system is engaged in is meant to reinforce or reproduce this identity. Patterns of interaction are circular and part of the system’s organization. An important characteristic of autopoietic systems is that they are organizationally closed systems, meaning that all possible states of activity must always lead to or generate further activity within the system (Mingers, 1995). Or, to put it differently, all activity must maintain autopoiesis to prevent the system from disintegration. The environment, which is created by the system itself, is therefore a projection of its own identity. The way the world is seen by the system is determined by the system itself, instead of being a reﬂection of an externally existing order. Living systems thus close in on themselves to maintain a stable pattern of relationships that are self-referential. The interaction of the system with its environment is always self-referential in the sense that it refers back to the system’s identity in order to facilitate self-production, i.e., to maintain autopoiesis. If living systems strive to maintain autopoiesis and relations with the environment are determined internally, then systems can evolve and change only along with selfgenerated changes in identity (Morgan, 1986). The theory of autopoiesis perceives the evolution of living systems as a result of internally generated change. Rather than suggesting that the system adapts to an environment or that the environment selects the system that survives, autopoiesis places its emphasis on the way living systems shape their own future. Changes in the system are only triggered from outside. What the eventual change will be and what in the environment can or cannot act as a trigger are determined by the actual living system. The changes that an autopoietic system can undergo are determined by the individual system so long as autopoiesis is maintained (Mingers, 1995). Living systems ‘‘are organized in such a way that their processes produce the very components that are necessary for the continuance of the processes’’ (Mingers, 1989). Maintaining autopoiesis is not just the reproduction of the same characteristics in similar circumstances, but rather the production of subsequent elements different from previous ones. However, the state of the actual system at a given time will determine the actual changes that the structure undergoes. In autopoiesis this is known as structurally determined. The internal structure determines what changes are possible to occur — only those that maintain autopoiesis — and thus how interaction with the environment will trigger changes in the system.

3 Autopoiesis as a Metaphor for Information Systems The application of the theory of autopoiesis, which has originated in biology, to the realm of the social is subject to controversial opinions. Maturana and Varela

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have refuted the idea that social systems are autopoietic. Some have suggested that social systems often portray characteristics similar to those autopoiesis explains in living systems, such as autonomy and the persistence of identity in contexts of massive change (Mingers, 1995). Opinions on the applicability of autopoiesis to areas other than living systems, such as groups, organizations, or society, seem to differ (Mingers, 1995; Brocklesby & Mingers, 2005; Huysman, Frisart, & Heng, 1995; Seidl & Becker, 2006). Some have directly applied the theory to social systems or have tried to slightly alter the theory of autopoiesis to ﬁt the social system as well. Inﬂuential has been the work of Niklas Luhmann on social systems as being autopoietic and in which communication plays a central role in the process of self-production (Luhmann, 1986; Seidl & Becker, 2006). The book by Von Krogh, Roos, and Slocum (1995) brought the theory in relation to knowledge management issues. We share the concerns about the direct applicability of autopoiesis to social systems and support Kickert’s remark that ‘‘y it is not so important whether a useful idea is an accurate translation of the original natural scientiﬁc model, but rather whether the idea is interesting and relevant y’’ (Kickert, 1993). In order to explore the relevance of the theory of autopoiesis to the area of information systems we use the theory as a metaphor. It has been successfully exercised and embraced in the ﬁelds of organization theory and information systems (Morgan, 1986; Walsham, 1991). The use of autopoiesis as a metaphor for information systems questions common conceptualizations of information systems. Traditional views on information systems are rooted in a mechanistic paradigm based on cybernetic systems thinking, while the increased attention to social issues call for ways of thinking beyond this ‘‘dead paradigm for living systems’’ (Ray, 1993; Blonk, 2002). In this paper we follow Kling’s conceptualization of an information system as a web of computing, a perspective that explicitly includes the social, historical, and political dimensions of the system besides the focal technology. Information systems ‘‘are not only ﬂexible information processing tools [but] their ‘‘shape,’’ the way they are used, the leverage they provide, and the interests they serve depend upon the interplay of stakeholders, resources, and social games within which they are deployed’’ (Kling, 1987). So, information systems are not just neutral entities, but they embody procedures, routines, power structures, and so on. They pre-select actions, relations, and possibilities. They embody ‘‘how things are done around here’’; they have an identity. In the next section we describe a case study of an information system that clearly acquired such an identity. It embodies how things were done one way rather than another. And its evolution shows a tendency to maintain itself, to keep up its identity regardless of its dynamic surroundings. The case shows that it was not just the hardware and the software, but also the social groups involved, the structures that were created, the style of thinking, and the way of managing that preserved the system as a whole. This whole socio-technical ensemble is what we attempt to analyze as if it were autopoietic. A system that continuously constituted its own boundaries, seemed to have acquired a high degree of autonomy, and was actively involved in reproducing and thus maintaining itself.

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4 Metaphor in Motion: A Case Study The case tells us about a ﬁnancial management information system at the Dutch Railways, which was developed in 1981 and continued to exist until 1993. The life of this system is described parallel to a massive process of change at the Dutch Railways which, in this period, was transforming from a state-owned and open-end ﬁnanced corporation to a privatized and commercial business. Within the context of these massive changes, the ﬁnancial information system continued to exist despite the ‘‘match’’ with its organizational context was lost. Seen in retrospect, it raises the question how a system that increasingly did not ﬁt its environment anymore continued to be supported and ﬁnanced, and was even redeveloped. For this, we need to understand the historical context of the organization and how it responded to change. The Dutch Railways is an old organization, which traditionally has been a stateowned company. Before approximately the 1980s the organization had been quite stable even though in the 1970s the Dutch Railways saw an enormous expansion of the organization, its infrastructure, and its activities. The organization had developed quite a strong culture, which provided the members of the organization security and stability. Employment was lifelong, and salaries and fringe beneﬁts were good. The organization itself was hierarchical, and administration and staff were centralized at the head ofﬁce. The regional units of the organization were mainly concerned with the operational processes to keep the trains running, such as personnel scheduling and maintenance work. The strong position of the unions and importance to acquire status and resources (which was dependent on number of employees) made personnel issues a very central focus. Financially, the organization was open-end ﬁnanced, which meant that all costs were accounted for by the Dutch government. Especially in the period of expansion of the 1970s, expenses had grown enormously which in fact started the process of restricting expenses, a process which would unfold into different successive ways of ﬁnancial management and eventually in the privatization of the Dutch Railways in the 1990s. Around 1980 the Dutch government decided to cut expenditure on the Dutch Railways by introducing yearly budget limits. As a consequence, the organization was restructured into a ﬂatter organization with more regional units and decentralization of responsibilities. The position of the regional accounting departments gained much in importance; parts of the ﬁnancial administration was now carried out regionally and the head of the department became member of the regional management team. In this context, the head of the central department of Planning and Control initiated a project to develop a new method of registrating costs and providing management information. This new accounting method (NAM) was meant to support the new budget-based organization, but the motivation to develop this method and its supportive system was more comprehensive; it also was an attempt to introduce a certain style of management based on personal as well as professional grounds, as the former head of the central department of Planning and Control states: It was purely my initiative. It was my opinion we would have to do this and that we should do this. There was no decent instrument for ﬁnancial management available.

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To realize the initiative, the head of the central department of Planning and Control hired an accountant and consultants who designed the basic structure of the system. A project group was started to further develop the system and to implement it in every regional unit. Young professional assistants were hired and trained to implement and use NAM. Every assistant was assigned the task to take care of the implementation of the system in the regional unit in which they were given a permanent position within the accounting department. The system was a revolution because for the ﬁrst time costs of operational processes were identiﬁed, registered, and managed. The project group worked under the supervision of the central department who could decide how the system was modiﬁed and further developed. In 1984 the system can be said to be implemented in every regional unit, but several regions showed quite some resistance. An important reason for this was that regional management disliked the fact that NAM made decisions ﬁnancially transparent, as a former accountant notes: The head [of the regional accounting department] wasn’t a very popular guy; somebody who was always harping on the money. By then, it was not very common to talk about money.

Further, the system, which still was manual, appeared to involve much work of a labor-intensive and simple calculative nature. A ﬁrst attempt to automate the system failed because of technological reasons. A spreadsheet appeared not to work on one of the ﬁrst IBM PCs when the data of the NAM system was entered. A new head of the central department of Planning and Control, somebody who also was involved in the development of NAM, asked the central information systems department to design and build a computer-based system to support NAM. Although that system, called NAMIS, took over much of the routine work, the underlying method of NAM was not changed. In 1988, not too long after the automated system was introduced, the budget structure was changed into what was called ‘‘contract management.’’ Instead of the Dutch government setting budget limits (which could be exceeded), the Dutch Railways were now required to plan their expenditures in advance thereby estimating the budget needed. The proposed budget was then recorded in a sort of contract between the Dutch government and different levels of management. The context in which the system was designed and operated had now changed quite signiﬁcantly in nature. NAMIS was designed to registrate and allocate costs and produce management information based on the recorded data. It was not designed to support the planning of expenditures a year ahead in order to determine the budgets needed. NAMIS also suffered from some functional shortcomings and rigid features, which had resulted in resistance and dissatisfaction with the system. Users had developed extensive procedures and routines to cope with the system’s rigidity and restrictions in order to do their work properly. This, then, was the background for the initiative to rebuild NAMIS. The project team, which did an initial study in 1990, concluded that NAM and its underlying logic should be maintained. The problems were identiﬁed in obsolete computer equipment, functional shortcomings and mistakes, and inadaptability to local requirements. Solving these problems by rebuilding the system was seen as the way out.

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Also in 1990 the organization, again, was restructured. The 15 regional units were grouped together into 8 larger units and were given signiﬁcantly more autonomy. Each region thus formed its own management team, and several new managers and controllers were hired, some of which came from outside the organization. Also, a controller from corporate level replaced the head of the central department of Planning and Control. Several newly formed regions started to develop their own information systems according to their own views and needs, as a former controller remarks: New managers in the regions with new controllers, often not from the original organization. Everywhere the wheel was re-invented, and everywhere different.

Shortly after the new organization was implemented, the new head of the central department of Planning and Control had to decide whether or not to continue the project of rebuilding NAMIS, history of which he was not familiar with. Several regional controllers under his supervision (who had been involved in the development and implementation of NAM) were in favor of the continuance. The decision was taken to continue the project as the head of the central department of Planning and Control comments: Was the system bankrupt in peoples minds? Of some, yes. But surely not of everybody. [We continued the project because] otherwise you have nothing, then you don’t know what happens out there, what kind of costs are being made.

Parallel to the start of the new organization, the project to rebuild NAMIS continued from early 1991 onwards. The project team consisted of information systems professionals and a small group of users who were selected by the project leader. Most of the participating users were already familiar with NAM for a number of years. During its development the system was presented very attractively to the organization — proposed future users could engage in a prize contest to give the system a name, a logo for the system was designed, frequent newsletters were distributed, and an expensive-looking manual for the NAM method and the new information system were distributed among the users. In the meantime the ‘‘old’’ NAMIS was still supposed to be used in the regions until the new system was ready. But the actual situation was very different. Several systems were being developed in different regions, and even the central department of Planning and Control started projects for a new ﬁnancial management information system. These systems were based on the new organization and the information requirements it imposed. However, they also were alternatives for the NAM and its supportive information systems. One region produced management information reports for the central departments using their own systems to generate the information and a word processor to imitate the layout of NAMIS reports. The project to rebuild NAMIS suffered from a number of drawbacks: The formal description of NAM had been lost and needed to be rewritten, there were performance problems, and a conﬂict with the supplier of the system delayed the project. The system was introduced in 1993, approximately two years after the reorganization and nearly four years after the project was started. During the implementation and user training

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the team noticed serious resistance. Two regional units refused to implement the system and a third wanted to postpone the implementation for one year. Subsequently, the general manager forced the regional units to implement the system. Even though the system was technically implemented it was never successfully used. One and a half year later the department formally responsible for maintaining the system didn’t have a clue who were still using the system, and they were very surprised to hear that some were still working with it. Yet, years after the collapse of NAM and its computer-based versions, several people still view the system as a good system that should have been used till today. The problems are not in the system, but in its environment — the organization, they say: [If you watch the developments now] there’s nothing new. What we introduced in ‘84, the organization might be ready to work with it. But it required two expensive information systems to get there. What is now presented as new or innovative, is just a revival if you’ve been in it long enough. (A former project member) [NAM] could have been extended and changed into a system that still would have been used. Then it would have been the current system. But we went through a different line of developments; they blew it, the reorganization, no support from management, everybody wanted their own system — and now again there is a trend toward a uniform system. The same result, but just a different path. (Former controller 1)

Even though the computerized information system NAMIS was not used throughout the organization, the underlying NAM method was still followed and used throughout various regions, although the method was used in different ways and to different levels. It was no longer a uniform method, and as such also reﬂected the new decentralized organizational structure. The embedded centralized structure and enforced uniformity of NAMIS had become out of sync with the new decentralized organizational structure of the Dutch Railways. Still the heart of the system, the NAM method, was still standing strong, and renewed itself during the years to come. Over the next few years, several initiatives had been taken to develop computer-based information systems to support different regional accounting methods, often based on NAM to a certain extent. One of these information systems has even been used in different regions. The controller who was involved in the development of this regional information system was one of the early assistants hired to develop the NAM method, now involved in the development of the information system that has made NAMIS obsolete. But he himself sees a strong continuation: The whole idea behind NAM is still alive and present in today’s systems, but it is experienced as something totally new. It’s so crazy, so funny. People who have never known NAM end up with the same sort of ideas. And that’s quite nice to notice. (Former controller 2)

5 Analysis and Discussion The metaphor of autopoiesis opens up an interesting and alternative perspective on how information systems evolve. It provides a view of information systems as social

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systems of interaction or social webs (Kling, 1987), which continuously try to maintain their self-image. In the theory of autopoiesis the identity of a system plays a central role and is continuously reproduced through the mechanism of autopoiesis. In contrast to more common perspectives on the evolution of information systems that stress the successive stages or states in a line of development, this metaphor draws attention to dynamics of the process, i.e., how systems evolve, and what it is that is evolving. The emphasis, in other words, is not on the (successive) outcomes, but on the underlying process that generates these outcomes. In analyzing the case using this metaphor, three themes are highlighted. First, how it is that a system creates its own environment and constitutes its own borders; second, how the organization of the system and its identity determine the conditions for its own functioning whether that will be success or failure; and third, how a system deals with changes and developments which are a threat to the system’s continuity. We discuss how the described information system constitutes itself as an autonomous and organizationally closed system aiming at the continuance of its very existence. We try to link the insights from autopoiesis to some literature in organization theory and information systems. The ﬁrst implication is that an information system continuously organizes itself including its perception of the organizational environment. It organizes its environment in such a way that the identity of the system will ﬁt the whole framework of perception and vice versa. The system enacts its environment by distinguishing only those aspects that make sense to the system, and it tends to ignore all that does not make sense to the system. The system thus decides what is relevant and what is not, it imposes a structure upon its surroundings that makes sense to the system itself, and it engages in self-referential interaction with its surroundings so that it is able to relate to perceived developments in order maintain itself. In this way the system establishes itself as an autonomous and organizationally closed system that determines its own boundaries, and selects what is proper to the system. Let us now assess how this happened in the described case study. The case portrays not just the development of a new accounting method, but also of the socio-institutional setup, which was needed to allow the system to function. After the system had been developed it was implemented in every region by newly hired assistants who were supposed to take care of the implementation at each site. Most of those new assistants later became controllers and entered an even better position to preserve the system. In order to establish itself, or to realize its identity, the system organized and created its own environment. Further, the introduced method itself is a way of organizing the environment because it discerned a large number or organizational processes, and how they needed to be ﬁnancially measured. This perception of the organization was recorded and reiﬁed in formal descriptions of the NAM method, elaborated user manuals were distributed in which organizational processes and the according ﬁnancial management information were speciﬁed. With the introduction, the regional units were made familiar with the system, i.e., the units were made to learn to view the organization as was prestructured by the system, and which not always was the preferred perception of the regional units themselves considering the resistance that was exhibited. Such documents and training programs

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reﬂect the system’s view and understanding of the organization, as well as its interests, which apparently closely linked to those of the accountants and controllers. As time went on, the system further actively organized its environment by introducing computer-based information systems that embodied the method and took over much of the administrative work. This is to be seen as a process of institutionalizing this particular way of organizing so that the system could maintain itself. The next issue which is raised by embracing the metaphor is what it is that is evolving, what is the identity that is reproduced, and how? The identity of the system determines how the system is organized, and the stability of the organization enables the system to reproduce itself. Autopoiesis thus refers to a duality between the structure of a system and what it aims to be. This is a recursive process in the sense that changes in the system’s organization have to cohere with the system’s identity. They are not separable, but evolve alongside. The process of organizing is the realization of an identity; both a system’s identity and its organization including its perception of the environment is the subject of evolution. Therefore, the system creates its own conditions for evolution; it shapes its own future. In the case, the identity of the system is the world of thought on which the system is based, and which is embodied in the system. This self-image of the system was continuously maintained and reinforced through self-referential processes. It contains a set of values, and patterns of thinking, a preconceived structure of this particular part of the organization of the Dutch Railways, and it is most clearly reﬂected in the three statements at the end of the case description in the previous section that talk of the ideas, the world of thought behind the system. Even though the computer-based information system had failed and NAM was no longer used as a system, people interpreted the world of thought behind the information system as still present and relevant, although it was realized in different systems. The identity was further emphasized and constructed through the prize contest to give the system a name and the logo that gave the system a face. The system was recognizable. A system needs to have such a cultural identity in order to deal with insecurity, uncertainty, and anxiety that are inherent in social life (Gagliardi, 1986). A system may give people (who are ‘‘in’’ the system) a sense of identity — a framework of theory, values, and related technology that enables people to make sense of their roles in the system (Schon, 1971; Gagliardi, 1986). This, at the same time, forms a condition for the maintenance of the system’s very identity. The system in our case study clearly involved people who continuously supported it, and were being involved in the maintenance and redevelopment of the system. During data collection some people spoke of ‘‘diehards’’ who kept on supporting the system, and one of the project members, very convinced, said: ‘‘It is my system.’’ The cultural identity and the system’s organization shape the construction of opportunities and threats; it determines how a system perceives what is to be seen as threats and opportunities. Problems, crucial developments, priorities, etc., in this perspective, are closely linked to the identity a system wishes to maintain (Morgan, 1986). In the case of NAM, the strong tendency to maintain the cultural identity of the system prevented it from incorporating the wider developments in the organization, and shaped the conditions for failure. An interesting perspective on the strive for

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stability in social systems and its resistance to change has been described by Schon in his book Beyond the stable state, as what he calls ‘‘‘dynamic conservatism’ — that is to say, a tendency to ﬁght to remain the same.’’ Schon acknowledges that resistance is inherent to social systems: ‘‘Resistance to change does not come from the stupidity or venality of individuals within the system; it is a function of the system itself’’ (Schon, 1971). He thus points to a similar characteristic of social systems as autopoiesis points to in living systems, which is the tendency to ﬁght to remain the same. The question is how social systems deal with threats and disruptive changes that may cause destruction of the system. Schon discusses ﬁve strategies how social systems exercise dynamic conservatism, i.e., how they deal with changes that disturb the stable state, or to put it differently, how they engage in the process of maintaining autopoiesis. First, the system tries to ignore the presence of a threat, and if it cannot be ignored it launches a counter attack or a preventive attack before the threat has materialized. If it does not succeed, it allows the threatened change a limited scope of activity and keeps it isolated. The fourth strategy is to absorb agents of change and turn to their own ends the energies originally directed toward change. And ﬁnally, if even that appears not to work, the system changes, but it allows the least change capable of neutralizing the intrusive process. The sequence of the ﬁrst three strategies we clearly recognize during the process of rebuilding NAMIS. The changes in the organizational environment were ignored at ﬁrst by keeping on using the same system. When it appeared that the changes could no longer be ignored, developments were attempted to be countered by redeveloping the NAMIS system. However, the developments were interpreted as a reason for optimizing the existing system without questioning the underlying method and assumptions, thus in such a way that the system’s identity could be maintained. But the changes embodied in the new organizational structure and new systems were of a different nature and could not be ignored. The system appeared not able to adopt the fourth and ﬁfth strategies, at least not in time, and it was abolished.

6 Conclusion In using the theory of autopoiesis as a metaphor to analyze the evolution of information systems we were able to draw attention to the generative mechanisms, the underlying dynamics that determine the continuity of a system. We have seen that a system is able to create its own conditions to be successful, or alternatively for its destruction. Autopoiesis, when used as a metaphor, may provide us with an interesting perspective on the process of evolution of information systems. The interesting insight this approach offers, and which was illustrated in the case, is that the main aim of systems is to maintain their identity despite the changes in their surroundings, in contrast to the common view of evolution as adaptation to external changes. It draws attention to how the system is structured to view the world rather than the way the world ‘‘is’’ (Walsham, 1991). This is not to say that such systems are static but rather that their evolution is determined by the identity the system has

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gained. Consequently, the evolution of information systems can be characterized as self-referential, autonomous, and organizationally closed. The theory does however also have its disadvantages of which we should be aware when using the theory for social systems (Walsham, 1991; Morgan, 1986). Organizational politics and issues of power are fully ignored. For example, the powerful position of the central department of Planning and Control in relation to the relatively weak position of the regional units has formed an important reason why NAM could diffuse itself through the organization. Also the decision to continue the redevelopment of NAMIS and its later introduction included negotiation, politics, and the use of power. Besides that, autopoiesis in the tradition of systems theories stresses unity as opposed to plurality and conﬂicts, which often seem to characterize social behavior and organizations (Walsham, 1991). But maybe that is the point where the metaphor loses its power. So far, we think it has proved a useful way of thinking about the evolution of information systems. Future research should further explore the underlying dynamics of the process of change, in order to enhance our understanding of how systems are (and should be) constructed, and how they evolve. Only then are we able to explain successes and failures of systems, because both success and failure are the results of the same underlying dynamics.

References Blonk, H. (2002). Changing the order, ordering the change. The evolution of an information system at Dutch Railways. Ph.D. thesis, Vrije Universiteit, Tinbergen Institute Research Series no. 264, Thela thesis, Amsterdam. Brocklesby, J., & Mingers, J. (2005). The use of the concept of autopoiesis in the theory of viable systems. Systems Research and Behavioral Science, 22, 3–10. Gagliardi, P. (1986). The creation and change of organizational cultures: A conceptual framework. Organization Studies, 7(2), 117–134. Huysman, M. H., Frisart, R., & Heng, M. S. (1995). Autopoiesis and organizational learning. Implications for information and information systems. Proceedings of the Third SISnet Conference, Berne, 18–19 September. Kickert, W. J. M. (1993). Autopoiesis and the science of (public) administration: Essence, sense and nonsense. Organization Studies, 14(2), 261–278. Kling, R. (1987). Deﬁning the boundaries of computing across complex organizations. In: R. J. Boland, Jr. & R. A. Hirschheim (Eds), Critical issues in information systems research. Chichester: Wiley. Luhmann, N. (1986). The autopoiesis of social systems. In: F. Geyer & J. van der Zouwen (Eds), Sociocybernetic paradoxes (pp. 172–192). London: Sage. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht: Reidel. Maturana, H. R., & Varela, F. J. (1992). The tree of knowledge. The biological roots of human understanding. Boston, MA: Shambhala Publications Inc.. Mingers, J. (1989). An introduction to autopoiesis: Implications and applications. Systems Practice, 2, 159–180.

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Mingers, J. (1995). Self-producing systems. Implications and applications of autopoiesis. New York, NY: Plenum Press. Morgan, G. (1986). Images of organization. Thousand Oaks, CA: Sage Publications. Ray, P. J. (1993). Dead paradigms for living systems. Proceedings of the First European Conference on Information Systems, Henley, 29–30 March. Schon, D. A. (1971). Beyond the stable state. New York: The Norton Library. Seidl, P., & Becker, K. H. (2006). Organizations as distinction, generating and processing systems. Niklas Luhmann’s contribution to organization studies. Organization, 13(1), 9–35. Varela, F. G. (1984). Two principles of self-organization. In: J. Ulrich & G. Probst (Eds), Selforganization and the management of social systems (pp. 25–32). Frankfurt: Springer. Von Krogh, G., Roos, J., & Slocum, K. (1995). Organizational epistemology. New York, NY: St. Martin’s Press. Walsham, G. (1991). Organizational metaphors and information systems research. European Journal of Information Systems, 1(2), 83–94.

Chapter 12

The Autopoiesis of Organizational Knowledge, Learning, and Memory Steffen Blaschke

1 Introduction Social systems theory (Luhmann, 1984, 1995) closely embraces the concept of autopoiesis which, originally, describes the recursive (self)-production of living systems (Maturana & Varela, 1980). Following this, autopoietic organization theory (Bakken & Hernes, 2003; Seidl & Becker, 2006) establishes a more specialized understanding of autopoiesis in terms of organization studies. The transition from the biological to the social realm, however, draws frequent critique. Some scholar suspiciously regard social systems theory as antihumanistic (Blu¨hdorn, 2000; Viskovatoff, 1999), for it neglects individuals in favor of interactions, organizations, and societies. Others deconstruct autopoietic organization theory with the argument that its deﬁnition of communication is ‘‘ﬂawed with an unavoidable mental dimension, namely the component of understanding’’ (Thyssen, 2003, p. 213). The validity of the critique rests mainly with the level of abstraction that the concept of autopoiesis maintains in both theories. In contrast to other schools of thought (e.g., natural, rational, and open systems theory; cf. Hatch, 1997; Scott, 1998), there is little empirical leeway to back up ideas such as that individuals and organizations are mutually exclusive autopoietic systems or, in more theoretical terms, that individuals and organizations are separated by operation yet coupled by observation. After all, there is no consciousness without communication as well as no communication without consciousness (Luhmann, 2002). In the following, I bridge the gap between theoretical contemplation and empirical research with a model and simulation of autopoietic organizational knowledge, learning, and memory. ‘‘Like voltage, current, and resistance, the terms knowledge, learning, and memory must be deﬁned in terms of each other’’ (Spender, 1996, p. 75). Together, then, these terms characterize the fundamental dialectic between structures Autopoiesis in Organization Theory and Practice Advanced Series in Management, 215–231 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006013

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and dynamics of organizations. The model draws heavily on social systems theory, thereby complementing autopoietic organization theory. The simulation stands in the tradition of mathematical approaches to organizational structures and dynamics such as Cohen, March, and Olsen’s (1972) Garbage Can Model of Organizational Choice, March’s (1991) Exploration and Exploitation in Organizational Learning, Carley’s (1992) Organizational Learning and Personnel Turnover, to name but a few. While the model is ﬁrst and foremost a mathematical conceptualization of the theory at hand, the simulation is a scientiﬁc inquiry ‘‘halfway between theory and experiment’’ (Waldrop, 1992, p. 63), a blend of deduction and induction. It is not an end in itself, however, but a means of manipulating the structures and dynamics of organizations in order to observe corresponding organizational behaviors. Therefore, the simulation ﬁndings allow for conclusions which are not necessarily evident from the theory and model alone. In closing, I discuss some of the ﬁndings in terms of future theoretical contemplation and empirical research.

2 Fundamentals In need of a theoretical access the autopoiesis of organizational knowledge, learning, and memory, I must brieﬂy review some of the fundamental concepts of social systems theory. In particular, these are (1) the distinction between system and environment, (2) the distinction between operation and observation, and (3) the complementing concepts of communication and expectation. At the heart of social systems theory is the premise that there are self-referential systems which constitute their basic elements and operations in self-producing fashion (Luhmann, 1986). In other words, autopoietic systems produce and reproduce their very elements by means of just their own operations. In case of psychic systems or individuals, the basic elements are single thoughts produced and reproduced in a network of consciousness (the mind) (Luhmann, 2002), while in case of social systems or organizations, the basic elements are single communication events produced and reproduced in a network of communication (the organization) (Luhmann, 1986). The elements and operations of psychic and social systems furthermore constitute the systems’ boundary to their environment. Hence, there are no systems without a corresponding environment, just as there are no environments without a corresponding system. As systems produce and reproduce themselves based on either consciousness or communication, they construct their environment as a negative correlate (i.e., as anything but consciousness or communication). Therefore, their environment is unique to themselves (Luhmann, 1995, p. 181ff.; Weick, 1969, p. 63ff.). The distinction between system and environment renders the latter as nonoperational. However, environments are not suspended to the autopoiesis of psychic and social systems per se; on the contrary, systems frequently perceive their environment as versatile, turbulent, or even chaotic. Environmental dynamics result from the further distinction between systems and other systems in their environment. Within

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this distinction, systems are mutually environment to each other; psychic systems are environment to social systems, and vice versa. (Surprisingly, already March and Simon (1958, p. 2) write, ‘‘for most people formal organizations represent a major part of the environment.’’) This circular exclusion deprives each system of any immediate inﬂuence on respective others since neither one can determine system/ environment distinctions apart from its own. Although systems cannot operate outside their boundary, they are by no means isolated in their existence. Psychic and social systems surmount their operational closure by observing other systems in their environment (Luhmann, 1986). In other words, individuals incorporate observations of organization in their network of consciousness, while organizations incorporate observations of consciousness in their network of communication. By and large, the reciprocity of boundary-spanning observations among psychic and social systems accounts for structural drifts (cf. Weick (1976) and Orton and Weick (1990) on loosely coupled systems). Moreover, the contingency of consciousness and communication forces individuals and organizations to attribute these drifts to environmental irritations (disturbances, perturbations, stimulations, etc.), mainly because they cannot think or communicate about everything and all. Therefore, psychic and social systems are necessarily less complex than their environment (Luhmann, 1995, p. 182). In addition to the observation of other systems in the environment, individuals and organizations incorporate observations of themselves in consciousness and communication, respectively. Indeed, (self-)observations are but operations to begin with. The distinction between operations and observations merely presents the reentry of past system/environment distinctions (i.e., previous consciousness or communication) into present systems’ operations (Luhmann, 1995, p. 36; Spencer-Brown, 1979, p. 49). Simply put, psychic and social systems uphold their autopoiesis by connecting past, present, and future operations in observation. Next to the system/environment and the operation/observation distinction, I must brieﬂy review the complementing (organizational) concepts of communication and expectation. With a particular interest in organizations, the respective complementing (individual) concepts of consciousness and expectation are left unaddressed. For the most part, however, the following review holds true for both organizations and individuals. Now, to ask with Luhmann (1992), What is Communication? Just like life and consciousness, communication is an emergent reality, a state of affairs sui generis. It arises through a synthesis of three different selections, namely, selection of information, selection of utterance of this information, and a selective understanding or misunderstanding of this utterance and its information. None of these components can be present by itself. Only together can they create communication. Only together — and that means only when their selectivity can be made congruent. Therefore communication occurs only when a difference of utterance and information is understood. That distinguishes it from mere perception of the behavior of others. In understanding, communication grasps a distinction between the information value of its content and the reasons for which the content was uttered. It can thereby emphasize one or the other side. It can concern itself more with the information itself or with the expressive behavior. But it always depends on the fact that both are experienced as selection and thereby distinguished. (Luhmann, 1992, p. 252)

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With information and utterance on the one hand and understanding or misunderstanding on the other, social systems theory furthermore distinguishes communication in two alternating roles. Information and utterance are selections of alter, understanding or misunderstanding is selection of ego (Luhmann, 1995, p. 85). Individuals participate in communication in either one of the two communication roles; technically speaking, however, their participation restricts to the boundary of consciousness. In other words, individuals are members of organizations only in that organizations observe them as participating in communication either as alter or ego. Figure 1 illustrates the autopoiesis of organizations and individuals. Communication ﬁnds a complement in expectation. In fact, communication condenses in expectation (Luhmann, 1995, pp. 96, 328), much like in ‘‘organizational communication evidence is replaced with conclusions drawn from that evidence, and these conclusions then become the ‘facts’ on which the rest of the organization acts’’ (March and Simon, 1958, p. 155). Expectation provides organizations with system/ environment distinctions (i.e., expectations) without the need to actually observe system/environment distinctions at any given time. (At this point, note that communication is to expectation what communication events are to expectations.) Expectation endows organizations with redundancy. Communication is no longer pressured to immediately reduce environmental complexity by observing (a greater portion of) everything and all. Organizations select information only when and where necessary (e.g., because there are no expectations to fall back to) and relay all further communication to expectation. At the same time that expectation eases the selection pressure of communication, it carries an inherent uncertainty with respect to

Figure 1: The autopoiesis of individuals and organizations (Blaschke, 2008, p. 65).

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environmental dynamics, for expectation may always indicate false system/environment distinctions. As social systems produce and reproduce themselves, expectation as a condensate of communication is under trial with each and every autopoietic episode, nevertheless. That is to say, expectations may always change in the face of communication events to come.

3 Theory In the past 50 years, management science and organization theory cover individual and organizational knowledge, learning, and memory in great detail (for a Trip Down Memory Lane, see Walsh, 1995). The following contemplations are easily understood as critique of mainstream theory, but this is rather circumstantial. Autopoietic organization theory is a mere alternative next to established schools of thought; in some parts, it breaks away from mainstream theory, in other parts, it complements management science and organization theory. For more elaborate critique and further reference to the concept of autopoiesis in terms of knowledge, learning, and memory, turn to von Krogh and Roos (1995), Magalhaes (1996), and Blaschke (2008). To begin with, I elaborate on knowledge. For lack of a better linguistic distinction, then, I discuss learning and unlearning in terms of learning. Last but not least, remembering and forgetting amount to the more generous term memory. Without further ado, Figure 2 illustrates the autopoiesis of organizational knowledge, learning, and memory. Knowledge is situated in the present organizational state of affairs, learning anticipates future operations, and memory contemplates past ones.

3.1

Knowledge

For most of the popular business press, knowledge is an all-encompassing concept. It is ‘‘anchored in the beliefs and commitment of its holder’’ (Nonaka & Takeuchi, 1995, p. 58); a ‘‘ﬂuid mix of framed experience, values, contextual information, and expert insight’’ (Davenport & Prusak, 1998, p. 5); and a ‘‘familiarity, awareness, or understanding gained through experience or study’’ (Allee, 2003). Any of these deﬁnitions of knowledge is of little value to further theory, unfortunately. Knowledge in terms of beliefs, experiences, values, information, insights, and so on, and so forth, is simply too broad an understanding (knowledge?) to work with. In contrast, mainstream literature in management science and organization theory ‘‘follows traditional epistemology and adopts a deﬁnition of knowledge as ‘‘justiﬁed true belief’’ (Nonaka, 1994, p. 15). This deﬁnition remains strong as of late despite examples of justiﬁed true belief which, in the end, fails to count as knowledge (Gettier, 1963). Although Nonaka (1994) himself emphasizes justiﬁcation over truth, I must abstain from any concept of true knowledge altogether. Truth only comes about a universal environment which, in turn, accounts for one and the same individual and organizational belief or, more precisely, belief structure. Needless to say, there is not

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Figure 2: The autopoiesis of organizational knowledge, learning, and memory. one truth, just as there is not one environment (Luhmann, 1995, p. 181ff.; Weick, 1969, p. 63ff.) or one reality (Berger & Luckmann, 1966; von Foerster, 2003). Nonetheless, a deﬁnition of knowledge as justiﬁed belief is considerably closer to the argument of social systems theory that knowledge is generalized cognitive expectation (Luhmann, 1995, p. 328). In other words, autopoietic organizational knowledge is expectation which provides communication with genuine (thus justiﬁed, thus generalized) system/environment distinctions. Recall that although expectation eases the selection pressure of communication, it carries an inherent uncertainty with respect to environmental dynamics (Section 2). Then again, knowledge as generalized cognitive expectation brings a certainty to communication in that it indicates which system/environment distinctions are genuine. Let me give an example. As many others, my wife and I generally expect the sun to rise in the East. Fortunately, our balcony faces in just that direction, and so the decision to have our morning coffee out on the balcony is usually rewarded with a little sunshine. The movement of the sun is common knowledge to many others besides my wife and me, of course. There is rarely a need for us to explicitly make the sunrise or sunset topic of our communication; still, it implicitly accompanies the decision to have our morning coffee out on the balcony (cf. Polanyi, 1958, on the matter of implicit and explicit knowledge).

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Learning

Management science and organization theory address organizational learning as early as the 1970s. Basic concepts, such as Single-loop and Double-loop Models in Research on Decision Making (Argyris, 1976), are still valuable to further theory. Organizations make decisions on a daily basis, after all. In case of a mismatch between any one of their decisions and whatever issue the decision aims at, organizations simply decide differently the next time around (single-loop learning). If the mismatch is more profound (e.g., continuous organizational failure), they inquire into the governing variables that bring about the very decision (double-loop learning). For reviews of the vast body of literature on organizational learning, turn to Hedberg (1981), Shrivastava (1983), Fiol and Lyles (1985), Levitt and March (1988), Huber (1991), Dodgson (1993), Miller (1996), Miner and Mezias (1996), and Easterby-Smith, Crossan, and Nicolini (2000). Social systems theory offers little additional insight on the matter of organizational learning. In general, learning ‘‘brings about partial structural changes in a system without interrupting its self-identiﬁcation,’’ Luhmann (1995, p. 111) writes. Singleloop learning, in this sense, changes system/environment distinctions inherent in communication. Double-loop learning, however, changes system/environment distinctions inherent in expectation, and only as a consequence communication changes, too. Partial structural changes in expectation come about the disappointment of knowledge. Indeed, a mismatch between a decision and the issue the decision aims at is nothing but a difference between system/environment distinctions made and observed. The partial structural change that follows the observation of such a difference (failure, error, etc.) is commonly known as learning from mistakes. Just as knowledge, learning is essential to organizations, not the least because of environmental dynamics. Although management science and organization theory promote an overall positive image of organizational learning (e.g., Argote, 1999; Senge, 1993), partial structural changes are risky to begin with. Learning promises decisions in tune with issues they aim at, for example, but there is no guarantee that different decisions are in fact better ones. Learning temporarily destabilizes organizations (Baecker, 2003, p. 183), in the meantime they are quite disoriented or even paralyzed (Hedberg, 1981), and sometimes it is plain disadvantageous (Herriott, Levinthal, & March, 1985; Levinthal & March, 1993; Lounamaa & March, 1987); just consider the possibilities of misinterpreting market demands, of adopting already dying technologies, or the like. Organizational learning receives its positive image retrospectively, that is, only after partial structural changes bring about genuine system/environment distinction. The possibility of autopoietic organizational learning is implicit in communication, always. Organizations produce and reproduce themselves based on the autopoietic mode of communication, and therefore partial structural changes are possible with each and every distinction between system and environment. The probability that changes take place, however, is a much more subtle subject. In this respect, organizational inertia (Hannan & Freeman, 1984) and organizational unlearning (Hedberg, 1981) are important concepts. Expectation may well be overgeneralized

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(i.e., inert), and organizations must unlearn their own past before any partial structural changes come about at all. These and other important concepts are beyond the scope of my argument which, in turn, is simply that autopoietic learning is essential to organizations, just as knowledge is, just as memory is.

3.3

Memory

As with deﬁnitions of knowledge and learning, mainstream literature in management science and organization theory deﬁnes memory in other terms than social systems theory. For example, Simon (1991, p. 128) writes, ‘‘much of the memory of organizations is stored in human heads, and only little of it in procedures put down on paper (or held in computer memories)’’ (for similar statements, cf. Anand, Manz, & Glick, 1998; Moorman & Miner, 1998; Olivera, 2000; Walsh & Ungson, 1991). In the light of some of the fundamental concepts of social systems theory (Section 2), organizational memory is certainly not stored in humans heads, simply because anything stored in human heads is at the most individual memory. Another case in point is that organizational memory is not stored at all, neither in humans’ heads nor anywhere else. While computer memories are all about storage and retrieval, as Simon (1991) readily points out, organizational memory is more about recognition and recall (von Foerster, 1981). If learning brings about partial structural changes (Luhmann, 1995, p. 111), then memory brings about partial structural changes, too. In case of learning, however, these structural changes take place in expectation, whereas in case of memory, they take place in communication. (Still, memory as a partial structural change of communication is not to be mistaken as single-loop learning.) Just as learning and knowledge, organizational memory accompanies each and every autopoietic episode of communication. At this, it either remembers or forgets past communication. In yet other words, organizational memory preserves some system/environment distinctions, while it interrupts others (Luhmann, 1996). Let me continue the above example. As usual, my wife and I decide to have our morning coffee out on the balcony. If we were miraculously to encounter a sunrise in the West, our expectation of the movement of the sun and any subsequent decision on where to have our morning coffee would most likely change, that is, we would have learned that there is no sunshine out on the balcony. While learning is possible with each and every decision made, it is probable only in that knowledge disappoints. Now, my wife and I decide to have our morning coffee out on the balcony with the memory of past decisions which rewarded us with a little sunshine. We remember the respective sunrise in the East and decide accordingly. Unfortunately, sleep deprivation due to late-night research (or any other reason) often enough fails our memory; we simply forget whether or not there is sunshine out on the balcony and decide to have our morning coffee in the kitchen. Of course, we can always check for the sun to rise in the East and thus jog our memory (i.e., remember, again).

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4 Model The above fundamentals of social systems theory and the introduction to knowledge, learning, and memory in the light of autopoietic organization theory are rather abstract, so far. The following model elaborates on these structures and dynamics of organizations. It bridges the gap between theoretical contemplation and empirical research by means of a mathematical conceptualization which, in turn, provides the basis of a later simulation (Section 5). Communication emerges from three different selections, namely, the selection of information, the selection of utterance of this information, and the selective understanding or misunderstanding of this utterance and its information (Luhmann, 1992). (Note that while Luhmann (1992) makes a case in point in always speaking of both understanding and misunderstanding, I omit the concept of misunderstanding from further discussion and illustration in order to keep the model as simple as possible.) Information, utterance, and understanding, then, are best illustrated with a binary value of 1 (for a selection) or 0 (for no selection). Hence, communication emerges from the selection of information, utterance, and understanding if — and only if — each one of these selections displays a value of 1. Think of the emergence of communication as information utterance understanding. Obviously, if any one of these selections displays a value of 0, no communication emerges to begin with. Now, communication synthesizes a decision, topic, or theme in observation of consciousness or expectation, that is, it observes a system/environment distinction. Communication may bring forth a decision to have the morning coffee on the balcony or in the kitchen, for example. Any such system/environment distinction generally implies a particular quality (balcony or kitchen?) which is best illustrated with a ternary value of 1 (for a particular quality of a decision, say, balcony), 1 (for a respective other quality of the decision, say, kitchen), or 0 (for no quality, i.e., no decision). To continue the example, the decision on where to have the morning coffee may display a value of 1 in case consciousness or expectation indicate the balcony as a suitable location, a value of 1 in case the kitchen is more preferable, or a value of 0 in case communication brings forth no decision at all. Think of the synthesis of communication as consciousness or expectation information utterance understanding. Table 1 illustrates the emergence and synthesis of communication on m dimensions.

Table 1: The emergence and synthesis of communication. m

1

2

3

4

5

6

7

8

9

Consciousness (Expectation) Information Utterance Understanding Communication

1 0 1 0 0 0

0 1 0 1 0 0

1 1 1 1 1 1

1 0 1 1 1 1

0 1 1 1 1 1

1 0 0 1 1 0

1 0 1 0 1 0

1 1 1 1 1 1

0 1 0 0 0 0

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The relation between the emergence and synthesis of communication is more evident on the assumption that ‘‘information — is a difference which makes a difference’’ (Bateson, 1987, p. 459). Information channels system/environment distinctions at the (spatial) boundary to consciousness (i.e., an individual participating in the communication role of alter) as well as at the (temporal) boundary to expectation. On the third, fourth, and eighth dimension (m ¼ 3, 4, and 8), for instance, communication emerges from information, utterance, and understanding and synthesizes a decision from consciousness. On the ﬁfth dimension (m ¼ 5), in contrast, communication emerges and synthesizes a decision from expectation. By now it is obvious that my wife and I like to have our morning coffee out on the balcony. Nonetheless, let me illustrate the above example in more technical terms. My wife says, ‘‘Honey, let’s have our morning coffee out on the balcony.’’ That is, her consciousness (alter) provides a system/environment distinction to information and utterance, whereupon understanding concludes communication. In turn, my consciousness (ego) accepts the decision in hope that she will bring me a freshly brewed cup of coffee. (More often than not, it turns out that communication concluded with a misunderstanding; somehow it is always me who brings her coffee.) If the decision to have our morning coffee out on the balcony is best illustrated with a value of 1 (as in the third dimension of Table 1), then a respective other decision, such as to have our morning coffee in the kitchen, comes about a respective other value (e.g., a value of 1 as in the fourth dimension of Table 1). Organizations produce and reproduce themselves on the autopoietic mode of communication. As far as communication produces decisions, organizations solve problems, accomplish tasks, and do business more efﬁciently than their members each by himself or herself. These problems, tasks, and the general business of organizations are all attributions (i.e., system/environment distinctions) of communication to the environment. If information, utterance, and understanding are best illustrated with binary values (1 or 0), and if consciousness, expectation, and communication are best illustrated with ternary values (1, 1, or 0), then the environment of organizations is likewise best illustrated with a binary value of 1 (for a particular quality of, e.g., a task) or 1 (i.e., a respective other quality of a task). Note that the environment always reﬂects one or another quality of a task (for a discussion of task complexity, cf. Carley, 1992) because communication constitutes the environment as the necessarily more complex side of the distinction. In similar vein, March (1991, p. 75) deﬁnes ‘‘an external reality that is independent of beliefs about it. Reality is described as having m dimensions, each of which has a value of 1 or 1.’’ With a conceptualization of the environment in place, Table 2 illustrates the autopoiesis of organizational knowledge, learning, and memory. Recall that autopoietic organizational knowledge is generalized cognitive expectation (Luhmann, 1995, p. 328). Technically speaking, it accompanies each of the m dimensions of communication with a value other than 0 (m ¼ 3, 4, 5, and 8, to be precise). On the third and ﬁfth dimension, expectation provides genuine system/ environment distinction; in effect, the decisions (communication) are in line with elements of the task (environment). While there is no generalized cognitive expectation on dimension 4, expectation provides a system/environment distinction of a quality other than the task on dimension 8. In observation of consciousness,

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Table 2: The autopoiesis of organizational knowledge, learning, and memory. m

1

2

3

4

5

6

7

8

9

Consciousness (Expectation) Communication Environment

1 0 0 1

0 1 0 1

1 1 1 1

1 0 1 1

0 1 1 1

1 0 0 1

1 0 0 1

1 1 1 1

0 1 0 1

communication brings forth a decision in line with the environment, still. The knowledge that accompanies communication, however, disappoints in the light of the task. Fortunately, the possibility of organizational learning and with a change in expectation is at close hand. Autopoietic organizational learning takes place in that expectation changes its quality (e.g., from a value of 1, as illustrated on the eighth dimension, m ¼ 8) to the quality of communication (i.e., to a value of 1). The probability of learning depends on a number of factors (e.g., organizational inertia), though it generally increases with the continuous disappointment of knowledge. That is to say, the longer expectation fails to provide communication with genuine system/environment distinctions, the more likely it is to change to whatever quality communication reﬂects. Now, learning is possible and indeed probable even if expectation reﬂects no quality to begin with (i.e., the value is 0, cf. m ¼ 4). No knowledge or, rather, non-knowledge disappoints in the light of the task, whereupon communication generalizes expectation. As is the case on the fourth dimension (m ¼ 4), expectation may only change its quality to that of communication, that is, its value of 0 may only change to a value of 1, despite the fact that knowledge is bound to disappoint. Therefore, learning is not necessarily advantageous. Table 2 further illustrates autopoietic organizational memory. Indeed, memory accompanies communication on all dimensions in that it either remembers or forgets system/environment distinctions. Remembering speciﬁcally takes place on the third, fourth, ﬁfth, and eighth dimension (m ¼ 3, 4, 5, and 8), whereas forgetting accounts for the remaining ﬁve dimensions (i.e., m ¼ 1, 2, 6, 7, and 9). Similar to learning, which brings about partial structural changes in expectation, memory brings about partial structural changes in communication, too. In contrast to learning, however, remembering whatever is forgotten or forgetting whatever is remembered is always possible, there is no need for knowledge to disappoint ﬁrst. The probability of remembering and forgetting depends on many the same factors that facilitate or impede learning (e.g., organizational inertia). With respect to the environment, memory is likely to partially change communication on dimensions 2 and 9 (m ¼ 2 and 9), that is, it may remember system/environment distinctions which presently are forgotten.

5 Simulation First and foremost, the above model of autopoietic organizational knowledge, learning, and memory offers a hands-on account of autopoietic organization theory.

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In addition, it provides the basis for (computational) simulation. Standing half way between theory and practice, simulation is a third way of scientiﬁc inquiry. In general, it takes three major steps: (1) the deductive development of a theoretical model, (2) the implementation of the model in an experimental design, and (3) the inductive analysis of the simulation results (Axelrod, 1997). Section 4 already outlines the ﬁrst step. Due to page constraints, I omit the implementation of the above model of autopoietic organizational knowledge, learning, and memory altogether. My recent work on the Structures and Dynamics of Autopoietic Organizations (Blaschke, 2008) contemplates this second step in detail. In the remainder of this section, I jump right to the third step and discuss some of the simulation results. Modeling operations and observations of psychic and social systems as binary and ternary vectors allows for a mathematical approach to the structures and dynamics of organizations (i.e., knowledge, learning, and memory). Equation (1), for example, denotes autopoietic organizational knowledge, OrgKnw(t), at time t, m 1X Comi ðtÞ Envi ðtÞ OrgKnwðtÞ ¼ m i¼1

(

0; 1;

Comi Envi 0 Comi Envi 40

(1)

Comi(t) returns the value the ternary vector for communication holds in dimension i, and Envi(t) returns the value the binary vector for the environment holds in dimension i. The index variable i runs from 1 to m, then. If communication fails to bring forth a decision (Comi ¼ 0) or the decision is different from the element of the task it aims at (Comi6¼Envi), then the equation yields a value of 0; in case of a decision in line with the task, it yields a value of 1. The sum of positive values (i.e., communication in line with the environment) over the number of dimensions (m) accounts for autopoietic organizational knowledge, for instance, 3/9 or 0.3, as illustrated in Table 2. While knowledge reﬂects the structure of communication, learning and memory reﬂect the dynamics of expectation and communication. Autopoietic organizational learning takes place with the partial structural change in expectation, that is, with a change in value from 0 to 1 or 1, from 1 to 1, or from 1 to 1. (In contrast, a change in value from 1 or 1 to 0 denotes a rare case of amnesia.) Likewise, autopoietic organizational memory takes place with the partial structural change in communication, that is, with a change in value from 0 to 1 or 1 (remembering) or, the other way around, from 1 or 1 to 0 (forgetting). The production and reproduction of communication with a value of 1 or 1 pertains to remembering, too, just as forgetting retains a communication with a value of 0. At this, autopoietic organizational memory preserves some system/environment distinctions, while it interrupts others (Luhmann, 1996). Figure 3 displays autopoietic organizational knowledge, learning, and memory or, rather, forgetting. The quantitative results of the simulation depend on initial conditions, implementation speciﬁcations, and the like, but the qualitative results are insensitive to these matters. Organizational knowledge at a level of 0.5 indicates that 50% of all decisions made are in line with the environment, learning at the same level

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Figure 3: Autopoietic organizational knowledge, learning, and memory. indicates that communication changes expectation at a probability rate of 50%, and if memory is ever to hit the level of 0.5, then communication remembers (or forgets) at a probability rate of 50%. More importantly than these, quantitative illustrations are the qualitative results. The latter support the picture of a learning organization, that is, the positive development of autopoietic organizational knowledge (cf. Senge, 1993, for a discussion of organizational learning versus the learning organization). At the same time, the level of learning decreases over time. Although counterintuitive at ﬁrst, the reason for less and less learning is simply that there is not much else to learn once organizations know most about the problems, tasks, or the general business they face. Autopoietic organizational memory complements the picture with a ﬁrst increase and later decrease in forgetting. (Although not displayed, remembering conversely decreases and increases.) In other words, organizations forget irrelevant knowledge to begin with and remember relevant knowledge later on. The basic simulation of autopoietic organizational knowledge, learning, and memory discloses implications with respect to theory. Luhmann (1996, p. 311) claims that forgetting is not an unfortunate adversity but rather the primary function of memory, whereas remembering is more or less an exception. This is well the case for organizations in ﬂux. However, as organizational structures and dynamics become inert over time, remembering takes over as the primary function of memory, thereby endangering organizations of being trapped in their own past (Blaschke & Schoeneborn, 2006). In other words, forgetting takes ﬁrst place in the light of a

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dynamic environment, for it interrupts unnecessary system/environment distinctions. Remembering turns prime in the light of a stable environment, for it preserves system/ environment distinctions in all certainty. Simulation allows for numerous other experiments, of course. For example, it answers questions regarding the effects of personnel turnover (Carley, 1992) or layoff (Blaschke, 2008) on the production and reproduction of knowledge. My intention is not to discuss these or any other effects in detail but to establish the model and the simulation thereof as a link between theory and practice. Therefore, I turn to some of implications with respect to practice.

6 Conclusion Social systems theory and autopoietic organization theory largely deny empirical research on individuals and organizations, as the critique readily points out (Blu¨hdorn, 2000; Viskovatoff, 1999; Thyssen, 2003). The model and simulation of organizational structures and dynamics, however, address issues of knowledge, learning, and memory beyond mere theoretical contemplation. They conceptualize the autopoiesis of knowledge, learning, and memory as essential to organizations themselves and, at the same time, allow for a better understanding (knowledge?) of organizations per se. Mathematical approaches to the structures and dynamics of organizations are quite common in management science and organization theory (e.g., March, 1991; Carley, 1992; Cohen et al., 1972; Harrison & Carroll, 1991). Practice makes good use of many such models, too. Organizations communicate their intellectual property in balance sheets (Sveiby, 1997), for example. Unfortunately, any such static account of knowledge is a snapshot at best and, more likely, produces a distorted picture altogether, not the least because organizations frequently know more than they are able to recall in a single year’s end survey. A dynamic account of knowledge is a more appropriate way to the analysis of autopoietic organizations, then. If organizations continuously keep record of their decisions and match them with the problems, tasks, or the general business they face, then they may report this production and reproduction of knowledge at any given time. The evaluation of decisions is almost natural to organizations, particularly to for-proﬁt corporations. Indeed, organizations frequently employ decision support systems, project management tools, and business planning software as a means to just this end. Alas, they rarely rely on a rigorous mathematical conceptualization of theory. Communication about knowledge is strenuous, but monitoring is ever the easier with new information technologies. For instance, collaborative authoring systems (e.g., wikis) provide a complete history of all changes made to documents which, of course, comprise decisions, if they are identiﬁed as such. Empirical research may tap into this resource of computer-mediated communication and process the data according to the model of autopoietic organizational knowledge, learning, and memory. Additional simulation may serve a number of purposes, for instance, the discovery of knowledge with respect to new environmental challenges or the prediction of learning and memory in the light of different future scenarios.

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Let me ﬁnish by returning to the above example on last time. Each and every morning, my wife and I face a task we only refer to as ‘‘coffee?’’ As simple as the task is, it actually requires a number of decisions, namely, who will get up ﬁrst to actually make the coffee, where to have it, and the like. At this, we rely on our knowledge about who is best to operate the coffee machine, about the sunrise (remember, we like to have our morning coffee out on the balcony), and many more system/environment distinctions. Knowledge, learning, and memory are implicit to our decisions; we hardly ever talk about it. If we were to assess the knowledge required to successfully accomplish the task (‘‘coffee?’’), we could simply document our decisions and how they turn out. ‘‘Honey, it’s your turn to make coffee’’ is another frequent sentence I hear from my wife, and the next time around I will put down somewhere that I made her coffee, indeed. As ﬂattered as I am to hear my wife praise my coffee, it only generalizes the expectation that she favors me making her coffee. But we both know that.

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Chapter 13

Autopoiesis: Building a Bridge between Knowledge Management and Complexity Robert Kay and Chris Goldspink

1 Introduction In this chapter we argue that a theoretical position derived from a combination of autopoietic theory and complexity theory provides a means for addressing two fundamental problems with the knowledge management (KM) concept. These problems are a lack of consistent epistemology — inadequate theorization about the nature of knowledge and a tendency to identify knowledge as residing primarily at the level of individuals. It represents an opportunity to move away from the reiﬁed view of knowledge that dominates most discussions of KM to one of knowledge which is deeply situated and contextualized. We argue that organizations are complex systems of a particular class; they comprise human (biological, reﬂexive) agents. This has important implications for the range and type of behaviors we can expect from organizations, but it also has implications for how we theorize about them. Perhaps because of its origins in the natural sciences, the epistemic implications of complexity applied to organizations have been appreciated by only a few researchers, Paul Cilliers prominent among them. The moment we wish to study or strategically inﬂuence complex systems which are comprised of human agents, we need to have a consistent framework for understanding what the deﬁning characteristics of this class of complex systems is and what its implications are. That consistent framework, we argue, is supplied by the theory of autopoiesis. Not only does autopoietic theory provide a coherent and compatible epistemological basis for the study of complex social systems, it also provides a means through which to address the micro–macro divide and therefore the basis for a ‘‘complex’’ view of KM. This chapter will begin by brieﬂy reviewing the way in which the barrier described above has been manifest in both the KM and complexity literatures. This will be followed by a brief description of the key concepts within autopoietic theory, as they Autopoiesis in Organization Theory and Practice Advanced Series in Management, 233–242 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006014

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relate to KM and complexity. The chapter concludes with an exploration of the implications of a complex/autopoietic approach to KM.

1.1

Different Approaches to Knowledge Management

Over the more than 15 years since the term knowledge management gained popular currency in organizations, considerable debate has ensued about how it may be conceptualized. Indeed, within organizational research, there would appear to be at least three broad streams of study relating to knowledge and its management (Sveiby, 1994; von Krogh & Roos, 1996), with many subthemes emerging under each as the ﬁeld has developed. Considering each stream from the perspective of the epistemological assumptions it embraces, the ﬁrst conceives of knowledge as an object. This stream is often associated with information science and information markets. von Krogh and Roos (1996) refer to this stream as the ‘‘Information Processing epistemology.’’ The key concern of this stream is with the codiﬁcation of ‘‘knowledge’’ into units of information that can be easily moved, sold, or attributed value in some form. The second stream of research views knowledge more as a process and is concerned with the behavioral aspects of organizational life and their effect on the retention and transfer of knowledge throughout the organization. von Krogh and Roos (1996) referred to this as the ‘‘Network Epistemology.’’ The key concern in this stream is with the different ways of connecting people within the organization, with information systems often playing a central role as supposedly the most cost-effective means by which this can be achieved. Epistemologically, as with the ﬁrst stream, a reiﬁed view of knowledge is adopted. Partly due to this, a further splintering of approaches has become evident:

that of the individual, where knowledge is seen as the property of individual people (Polanyi, 1958, 1967), and that of the organization, where the organization itself is viewed as having knowledge (Walsh & Ungson, 1991; Weick & Roberts, 1993).

The subthemes created by these distinctions have each spawned considerable literatures of their own, thus making a cohesive view of knowledge in organizations more difﬁcult to discern. This confusion in relation to which level knowledge should be ascribed reﬂects a particular manifestation of the wider micro-to-macro problem (Goldspink & Kay, 2004). In this case it manifests in the debate about whether knowledge is wholly or predominantly a micro phenomena (i.e., associated with individuals), or a macro phenomena (associated with systems of individuals). This dichotomization is an inevitable consequence of conceiving of knowledge in a reiﬁed manner — i.e., as an object. Our aim should be, therefore, to frame knowledge in such a way that we can coherently conceive of the relationship between knowledge held by the constitutive elements of organizations (people) and the emergent capabilities that

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result from their interaction (i.e., organizational knowledge). As such we seek an understanding of the way in which knowledge and patterns of interaction mutually inﬂuence each other across logical levels of analysis. A third stream of research in KM goes some way toward this. This stream identiﬁed by von Krogh and Roos has received relatively little attention in the literature, and is termed the ‘‘self-referential epistemology.’’ This view is markedly different from the two perspectives described above and assumes that knowledge is a: y private, history dependent process in each one of us. What knowledge is for you is only raw data for everybody else. Thus, you have always organizational knowledge with somebody, and the means to this is language. (von Krogh & Roos, 1996, p. 334)

In terms of bringing together complexity and KM, this third stream is signiﬁcant for one simple reason: it moves the discussion of knowledge away from the reiﬁed perspectives common to the other streams of KM research to a perspective which is more aligned to the approach discussed by Cilliers (2000b).

1.2

Cognitive Foundations of Knowledge: The Implications of Complexity

Knowledge begins with cognition, and our cognitive capacity is inherent in our biology. Any theory of KM and consequently any application of complexity needs to be consistent with our understanding of the biology of cognition. If this is accepted, then one of the ﬁrst things we need to rethink is the widespread adoption within KM of the representationalist view of cognition. The idea that we capture understanding of the world in our brains — using some form of symbolic representation as with digital machines — is no longer defensible; we know our brains simply do not work this way. Continuing to use such a metaphor supports the continued idea that KM can be associated with the capturing of salient representations about the real world and the idea that the more ‘‘accurate’’ the representation the better the knowledge. This is inconsistent with what biology tells us about how we ‘‘know’’ anything. Once this idea is rejected, with it falls the idea that human action is guided primarily by a rational weighing of facts about the real world and/or on the calculation of some ‘‘utility’’ by each individual. This assumption is of course the foundation stone of most management and organization theory. Within KM, complexity theory is primarily argued to be relevant to understanding macro organizational dynamics. What we are arguing here is that the macro-level dynamics of organizations emerge from the activities of micro-level human agents. Complexity therefore has further relevance in that those human agents are themselves macro phenomena — emerging from the interaction of microbiological agents — cells. To build a bridge between complexity and KM, we ideally need a set if internally consistent theories that allow us to traverse this cascade of micro–macro boundaries — from our molecular foundations to the highest order social phenomena. At the same time we need to reject the convenient way in which sociologists and organization theorists have avoided becoming entangled in the micro–macro problem by quarantining different levels of analysis. While convenient

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it means that we can never really explain the emergent nature of social phenomena — we can only provide incomplete descriptions. In this context, the ﬁeld of complexity is providing us with the tools needed to move beyond this situation. Our goal in this chapter is to set a model of organization which has the following attributes:

It is conceptualized so as to capture the salient characteristics of human organizations yet sufﬁciently open to use it to reappraise a wide spectrum of individual and social behavior. It avoids characterizing organizations in ways inconsistent with the insights currently available from biology and complexity. It supports the exploration of organizational properties — in particular, the interrelationship between micro and micro levels — using methods intrinsically suited to the study of complex systems — such as computer simulation.

2 The Foundations of the Bridge: A Biologically Grounded Theory of How We Come to Know While biology is continuing to expand our understanding of human cognition, there exists one body of work which serves to bring together much of what has been understood to date and — uncommonly — which does so in a manner that is consistent with complexity theory. This is the theory of autopoietic systems developed by the Chilean biologists Humberto Maturana and Francisco Varela (1980). Although it is not possible in this chapter to fully describe the theory, a brief outline of the core concepts within autopoiesis is provided so that subsequent discussions may be understood. The theory was developed to provide explanations of the nature and characteristics of living systems (biological cells and metacellular organisms). The central idea is that living systems are characterized by their self-production; the components of the system producing the components of the system. The process requirements for selfproduction then constrain the way in which individuals can interact with and ‘‘know’’ their environments. Within autopoietic theory, an individual’s behavior is determined by particular states of nervous system activity (Maturana & Varela, 1980); this activity is deﬁned by the concept of operational closure, which presupposes that in all cases nervous system activity results from and leads to further nervous system activity in a closed cycle (Maturana & Varela, 1980). Possible and actual changes in state of the nervous system are therefore deﬁned by the nervous system’s structure and not external forces. External or environmental forces may act as triggers for change, but it is the nervous system’s structure that dictates which forces can be a trigger (Mingers, 1991). Therefore, changes to the structure of one person’s nervous system, and consequently their behavior, will be unique to that person. The environmental perturbations that act as a change trigger in one person will not necessarily trigger a change in another, or if they do, the change that is triggered may take a different form and/or have

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different implications for the viability of that person in his/her environment, given his/her history. Although the nervous system is operationally closed it is plastic, its structure changes over time, and it is this quality that allows for changes in behavior and subsequently what we describe as learning (Mingers, 1991). Therefore as the nervous systems structure changes, so too will the potential range of behaviors that its structural determinacy makes possible. The term used for this history of structural change is ontogeny (Maturana & Varela, 1992). Where there is a history of recurrent interaction between two individuals, a structural congruence can develop — they become structurally coupled. We have argued (Goldspink & Kay, 2003, 2004) that structural coupling is the mechanism by which all social structures emerge including what we refer to as organizations. Thus, structural coupling constitutes the building block for organizational knowledge. This description of autopoietic theory should only be considered as a cursory introduction to some of the major concepts. The signiﬁcance of these ideas, however, becomes apparent when they are applied to the notions of cognition, knowledge, and organizations, as they deﬁne the process by which the individual comes to know of their environment and orient themselves within it.

3 Implications for Understanding Cognition Varela, Thompson, and Rosch (1992) have developed a theory of cognition consistent with the autopoietic nature of living systems. They state that cognition takes place whenever an organism behaves in a manner consistent with its maintenance and without loss of identity, i.e., without loss of any of its deﬁning characteristics. This theory challenges the most commonly accepted view of human cognition — that of cognitivism or representationalism. Speciﬁcally they state: The central intuition behind cognitivism is that intelligence — human intelligence included — so resembles computation in its essential characteristics that cognition can actually be deﬁned as computations of symbolic representations (Varela et al., 1992, p. 40; our emphasis].

Thus, representations are deﬁned teleologically they are intentional, and are ‘‘about something for the system’’ (Varela et al., 1992, p. 44). Cognitivism constructs a duality: the environment is experienced as a facticity and acted upon directly, but is also conceived and symbolically represented in the mind. Mind and behavior are linked as hypothesis and experiment. This way of understanding human cognition is being increasingly criticized from within biology and other disciplines such as artiﬁcial intelligence (AI) and by complexity theorists (Cilliers, 1998, 2000a; Stacey, 2001). Both AI and complexity theories have given greater impetus to connectionist theories of cognition (Cilliers, 1998). Here emergent structure or pattern arises from massively interconnected webs of active agents. Applied to the brain, Varela et al. point out: The brain is thus a highly cooperative system: the dense interconnections amongst its components entail that eventually everything going on will be a function of what all the other components are doing. (1992, p. 94)

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It is important to note that no symbols are invoked or required by this theory. Meaning is embodied in ﬁne-grained structures and patterns distinguished throughout the network. Symbolic approaches require a direct mapping — symbol to symbolized: implying the availability of a tangible reference point, or at least a referent that can be mapped with minimal ambiguity. Connectionist approaches can derive pattern and meaning by mapping a referent situation in many different (and context dependent) ways. Meaning in connectionist models is embodied by the overall state of the system in its context. It is implicit in the overall ‘‘performance in some domain.’’ Needless to say, if knowledge is not embedded in symbols, ‘‘it’’ cannot be stored in a database. Varela et al. note that: y an important and pervasive shift is beginning to take place in cognitive science under the very inﬂuence of its own research. This shift requires that we move away from the idea of the world as independent and extrinsic to the idea of a world as inseparable from the structure of [mental] processes of self modiﬁcation. This change in stance does not express a mere philosophical preference; it reﬂects the necessity of understanding cognitive systems not on the basis of their input and output relationships but by their operational closure. (1992, p. 139)

They go on to argue that connectionist approaches, while an advance on cognitivism, are not consistent with an approach which views biological agents as operationally closed (i.e., as autopoietic) in that ‘‘y the results of its processes are those processes themselves’’ (1992, p. 139). They assert: Such systems do not operate by representation. Instead of representing an independent world, they enact a world as a domain of distinctions that is inseparable from the structure embodied by the cognitive system. (1992, p. 140)

It is this theory of cognition that is reached by following the implications of the autopoietic view of living systems. There is no objective independent view of a world that can be captured in symbols having widely shared meaning and which can be captured, stored, and managed. Knowledge is ‘‘in the system.’’ Cognition as ‘‘enaction’’ implies an intertwining of experience and conceptualization which results from the structural coupling of an autonomous organism and its environment. Here environment recedes from determinant of knowledge to constraint. Intelligence moves from problem-solving capacity to ﬂexibility to enter into and engage with a shared world. It is intrinsically and necessarily social and contextual. The advantage of linking the theory of autopoiesis and complexity is that it provides a consistent framework that links both the constitutive (micro) and emergent (macro) dimensions of social organization in a manner consistent with what we understand about the deﬁning character of humans as biological and reﬂexive linguistic agents. Autopoiesis provides a model of how social phenomena emerge from the complex (and nonlinear) interplay between the heterogeneous (in having unique ontogenies) agents (people) which make it up. From this perspective, organizations or social systems can be seen as a speciﬁc class of complex systems and it is autopoiesis which clariﬁes the distinguishing characteristics of this class, in particular the linguistic/ reﬂexive character of social agents.

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4 The Nature and Role of Knowledge in Organizations Maturana and Varela have provided a biological grounding point for a complex systems understanding of knowledge in organizations. In this understanding, knowledge is deﬁned as a process of bringing forth ‘‘a’’ world. That world is the lived experience of the individual as he/she responds to his/her environment. Within this conceptualization, the notions of ‘‘doing,’’ ‘‘being,’’ and ‘‘knowing’’ are all bound into the single notion of knowledge and all are subject to structure-determined processes of change. The range of behaviors available will depend on the individual’s history of interaction with others — his/her ontogeny. As the nervous system’s structure changes in response to the environment, so too will the potential range of behaviors that its structural determinacy makes possible. As an observer we might call someone ‘‘more knowledgeable’’ if, after observing them, we notice that they generate a wider range of behaviors or are more successful at satisﬁcing the constraints they confront in that environment. Over time a human being may extend the behavioral repertoire he/she can generate and this we call learning — the gaining of knowledge. Knowledge may therefore be considered as the range of actual and potential behaviors that an individual may generate to respond to and remain viable within any given environment. This is however a judgment made by an observer and is a comment on that observer’s assessment of the quality of the responses the subject generates. The observer is noticing a macro (emergent) pattern and ascribing to it certain qualities. This brings us to how we may understand the relationship between individual learning (as described above) and an organization’s capacity to survive in its environment. We have so far considered the pathway by which order is generated bottom-up — from micro to macro in a way that reconciles it with biological constraints and describes the particular mechanisms that operate with biological agents — humans. But what of the top-down, macro and micro processes; how can these be resolved within this framework? Within autopoietic theory this top-down process is explained through the dynamics of the individual’s relationship with his/her environment. The ontogeny of each individual, while unique to that individual, is also a product of his/ her interactions with others — it is a co-evolved structure which is the product of structural coupling. Each agent is constrained to interact in a limited way not by the other agents in a direct causal sense but by virtue of its structural determinacy. The individual adjusts his/her behavioral repertoire as a function of his/her interactions with those other agents (his/her nervous system is now geared to produce a learned limited subset of responses). In this sense the collective aspect of knowledge is ‘‘in’’ each agent — it is reﬂected in the constraints embedded in his/her nervous system. What we have described so far could be applied to any social animal. There is however another pathway between macro and micro that is unique to humans.1

1

It may apply to other organisms which have evolved a capacity for self-awareness, such as some apes and cetaceans and possibly elephants, but the evidence is not yet clear on this, perhaps due to our inability to create a common language with them.

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The capacity in humans for reﬂexive self-awareness is central to this. Maturana and Varela attribute this capacity to the development of a critical level of plasticity in the nervous system. It is associated with the capacity to ‘‘coordinate the coordination of action’’ in and through language. A human agent may become an observer of self and of others and the observations he/she makes play a role in the social process. One of the implications of this view is that ‘‘knowledge’’ relates to an observed capacity to remain viable in an environment. It is an ex-post attribution by an observer and as such it plays no role in any organizational process unless:

the observer is a participant in the process (i.e., is one of the agents of the network which is giving rise to the observed pattern) or the observer was external, but becomes a part of that system by communicating his/ her observations such that those being observed respond by changing their behavior.

Note that this mechanism highlights a distinctive feature of human complex systems — an evolved capacity to distinguish self from other and to be reﬂexive — i.e., observe macro pattern and respond to it thereby changing the micro process and potentially, at the same time, contributing to a change in that macro pattern. Of course every human agent that makes up an organization is an observer. The distinctions and attributions made by each will be based on their unique ontogeny. However, as this ontogeny is a product of a shared process (resulting from their structural coupling with others) it is both a product of and the means for maintenance and generation of organizational knowledge. Note also that none of the distinctions made by an agent can be seen a priori to have a privileged position — none represent a ‘‘true’’ knowledge. Different distinctions made by different participant/observers will have different implications for the trajectory of the organization and its viability in any environment. Those implications are, however, dependent on the time and place of their occurrence and the state of the system as a whole at that time — they cannot be anticipated in advance. As each agent has a unique ontogeny, the macro observation that each makes will itself be unique (this is to say that the macro pattern will look different and have different meaning from the reference point of different agents) and hence will also have different behavioral implications. In this sense the pattern is instantiated uniquely in each and every agent and yet is also a product of this process. It is the difference (at the micro level) which gives rise to the commonality (at the macro level) because the difference at the micro level is itself a product of the history of arriving at the commonality at the macro level. Looked at another way, knowledge ‘‘bootstraps’’ from ignorance as agents act in a common environment and enter into recurrent interaction as they seek to remain viable in that environment. What does this imply about the constitutive nature of an ‘‘organization’’ and what the literature refers to as organizational knowledge? The ﬁrst observation that needs to be made is that knowledge, existing in patterns of distinctions, requires a distinguishing entity, i.e., a living system. As distinctions themselves, ‘‘organizations’’

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are not entities in a distinguishing sense (Kay, 2001). Indeed this point is emphasized in the ongoing debates within the literature on autopoietic theory regarding the ontological nature of organizations. In our view, to consider organizations as knowledgeable in themselves constitutes an inappropriate reiﬁcation which denies the basis for their emergence in and through the structural coupling of the humans which give rise to them. Organizations are distinctions not distinguishers. This is to say organizations cannot ‘‘have’’ knowledge. That organizations can be distinguished by an observer to have changed their patterns, exhibiting variation, selection, and retention processes (Aldrich, 1999) in a purposeful way is to confuse outcome with process. Complexity theory can aid the development of our understanding of the process by which these changes occur. However, it can only do this if we are mindful of what the general principles of complex systems translate to within biological and, more speciﬁcally, human systems. The reﬂexive and recursive nature of this process, as we have seen above, requires a clear epistemology accounting. Without this accounting it is easy to become confused and to see knowledge as a cause rather than the endpoint of a process.

5 Conclusion Autopoietic theory combined with complexity theory has been demonstrated here to have profound implications for our thinking about knowledge and organizations and hence KM. When applied to the domain of social action, the resulting theory implies the need to adopt a radically different view of the origins and nature of knowledge. This is because when we consider that organizations are complex systems of a particular class — ones comprised of human (biological reﬂexive) agents, we are drawn to a very different understanding of the origins of social structure and hence the nature of organizations as well as the nature of cognition. As well as causing us to rethink the implications of complexity for knowledge at an individual level, this approach requires us to question what we understand about the constitutive nature of an ‘‘organization.’’ We are compelled to consider the organization as a distinction made by an observer. As a distinction (and not a thing), it cannot itself make distinctions and so cannot ‘‘have’’ knowledge. Here we have presented a view of knowledge where what the observer identiﬁes as ‘‘knowledge’’ is an attribution based on the observation of coordinated behaviors at some level of the organization. Knowledge does not cause anything — it is not a basis for, nor the origin of coordinated behavior; it is an attribution that denotes (for the observer) the presence of some attribute in the quality of interaction being observed.

References Aldrich, H. (1999). Organizations evolving. London: SAGE.

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Cilliers, P. (1998). Complexity and post-modernism: Understanding complex systems. London: Routledge. Cilliers, P. (2000a). Rules and complex systems. Emergence, 2(3), 40–50. Cilliers, P. (2000b). Knowledge, complexity and understanding. Emergence, 2(4), 7–13. Goldspink, C., & Kay, R. (2003). Organizations as self-organizing and sustaining systems: A complex and autopoietic systems perspective. International Journal of General Systems, 32(5), 459–474. Goldspink, C., & Kay, R. (2004). Bridging the micro–macro divide: A new basis for social science. Human Relations, 57(5), 597–618. Kay, R. (2001). Are organizations autopoietic: A call for new debate?. Systems Research and Behavioral Science, 18, 461–477. Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: The realization of the living (vol. 42). Dordrecht: BSPS, Reidel. Maturana, H., & Varela, F. (1992). The tree of knowledge: The biological roots of human understanding. Boston, MA: Shambhala. Mingers, J. (1991). The cognitive theories of Maturana and Varela. Systems Practice, 4(4), 319–338. Polanyi, M. (1958). Personal knowledge. London: Routledge and Kegan Paul. Polanyi, M. (1967). The tacit dimension. London: Routledge and Kegan Paul. Stacey, R. (2001). Complex responsive processes in organizations: Learning and knowledge creation. London: Routledge. Sveiby, K. (1994). Towards a knowledge perspective on organization. Doctoral dissertation, University of Stockholm, Sweden. Walsh, J., & Ungson, G. (1991). Organizational memory. Academy of Management Review, 16(1), 57–91. Weick, K., & Roberts, K. (1993). Collective mind in organizations: Heedful interrelating on ﬂight decks. Administrative Science Quarterly, 38, 357–381. Varela, F., Thompson, E., & Rosch, E. (1992). The embodied mind. Cambridge, MA: MIT Press. von Krogh, G., & Roos, J. (1996). The epistemological challenge: Managing knowledge and intellectual capital — Editorial and overview. European Management Journal, 14(4), 333–337.

Chapter 14

Autopoiesis as the Foundation for Knowledge Management Paul Parboteeah, Thomas W. Jackson and Gillian Ragsdell

1 Introduction Knowledge management aims to increase an organization’s competitive advantage through the collective management of its employees’ knowledge. In the past, knowledge management was very technologically oriented, with a focus on data mining, software, and artiﬁcial intelligence, but in recent years there has been a move toward incorporating social aspects. As knowledge management evolved into its second era, the focus shifted to deﬁning knowledge, developing frameworks, and implementing content management systems. The current knowledge management era (third) appears to be more integrated with an organization’s philosophy, goals, and day-to-day activities, and is also the ‘‘softest’’ with regards to a people-oriented approach (Metaxiotis, Ergazakis, & Psarras, 2005; Wiig, 2002). As knowledge management moves further into the third era, no theoretical foundation exists. As will be seen, knowledge is an unmanageable, nontransferable entity that cannot exist outside a person’s brain (Abou-Zeid, 2007). As such it is not possible to deﬁne the concept of knowledge, nor even desirable, and this is in direct contrast to ﬁrst generation knowledge management, which aimed to accurately deﬁne the concept of knowledge (Metaxiotis et al., 2005). The focus on frameworks (Holsapple & Joshi, 1997), systems (Hasan & Gould, 2003), and technology (Liao, 2003) that dominated second-generation knowledge management is also not compatible with the current understanding of knowledge (Abou-Zeid, 2007), suggesting that systems cannot directly manage knowledge. With ﬁndings from an international delphi study suggesting that (Scholl, Konig, Meyer, & Heisig, 2004) autopoiesis is one of the most important theoretical advancements for knowledge management, the scene is set for the application of autopoiesis to knowledge management. Maturana and Varela developed autopoiesis in the 1960s as a way of deﬁning living systems. Autopoiesis proposes that living Autopoiesis in Organization Theory and Practice Advanced Series in Management, 243–261 Copyright r 2009 Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1877-6361/doi:10.1108/S1877-6361(2009)0000006015

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systems are discrete, autonomous systems whose identity remains constant through the continual maintenance of their organization. Evidently a ‘‘systems’’ based theory, autopoiesis lends itself nicely to being applied to other domains. Organizational learning, which is often considered a ‘‘soft’’ aspect of knowledge management, will undoubtedly beneﬁt from autopoietic theories detailing learning, adaptation, and evolution. Epistemological debates can be settled using the scientiﬁc ideas autopoiesis proposes about knowledge and its structure. The application of autopoiesis to knowledge management also brings with it the added bonus of giving knowledge management, a sound theoretical foundation, subsequently conﬁrming the need for an interdisciplinary approach to the current problems in knowledge management. However, autopoiesis cannot be randomly applied to knowledge management or organizational learning; a rigorous and coordinated method is needed, and this is provided by a matching methodology. Matching is a two-step process, comprised of theoretical discourse and inscription. Theoretical discourse is the uniﬁcation of two theories through frequent dialogue. The output is a new lexis, unique to the merging of the two domains. The lexis then feeds into the inscription process, which captures and makes an object out of the knowledge previously constructed through the theoretical discourse. Matching is different from positivist and interpretivist approaches to research due to this inscription process. Essentially, knowledge is presented in such a way that it can help other theory building attempts (in this case, knowledge management). This chapter will, through the process of inscription, give third era knowledge management initiatives a theoretical foundation, such that knowledge management will carry greater scientiﬁc weighting.

1.1

Introduction to Autopoiesis

Autopoiesis developed as the result of two questions: ‘‘how do I do what I do as an observer in observing?’’ and ‘‘what began three thousand eight million years ago so that you can say now that living systems began then?’’(Romesin, 2002). The ﬁrst question is based on the notion that we do not exist in an objective, independent world, for which we create our own representations, whilst the second question raised the issue that prior to autopoiesis, there was no conceptual explanation of what made something living. The answer to these two questions gives rise to autopoiesis, which starts from the premise that all living systems are discrete, autonomous entities that operate as self-sufﬁcient, closed molecular systems open to the ﬂow of molecules (Romesin, 2002). The exact moment in time when living systems ﬁrst appeared on earth would be when a network of molecular processes came together to form a closed network, forever then self-replicating itself until its death. From the realization of what made a cell living, it is possible to deﬁne an autopoietic entity as: a machine organized (deﬁned as a unity) as a network of processes of production (transformation and destruction) of components that produces the components which: (i) through their

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interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network. (Maturana & Varela, 1980)

As such, the key feature of an autopoietic entity is the constant maintenance of its organization, or network of processes that realize the entity as living. There are four consequences of an entity being autopoietic: autonomy, individuality, organizational closure, and self-speciﬁcation of boundaries (Maturana & Varela, 1980). Autonomy is the ability of an entity to specify its own laws and the behavior it exhibits (Maturana & Varela, 1998). The view that living entities are autonomous also contributes to the individuality of living entities. Maintaining their organization as autopoietic, living entities are also actively maintaining their identity (Maturana & Varela, 1998). Organizational closure is an essential feature of autopoietic entities, if they are going to remain living; if they did not maintain their autopoietic organization, they would disintegrate and die. However, just because a system is organizationally closed does not mean it cannot receive physical inputs (Mingers, 1995). An autopoietic entity is also able to specify its own boundaries. In the case of a cell, the internal dynamics produce the necessary components for the boundary, while at the same time, the boundary contains the processes of self-production (Maturana & Varela, 1998). As outlined by Varela, Maturana, and Uribe (1974), there are also six principles of autopoiesis, which are presented in Figure 1. These six principles were developed to 1. Determine, through interactions, is the unity has identifiable boundaries. If not, the unity is indescribable, and we can say nothing. 2. Determine if there are constitutive elements of the unity, that is, components of the unity. If not, the unity is an unanalyzable whole. 3. Determine if the unity is a mechanistic system, that is, the component properties are capable of satisfying certain relations that determine in the unity the interactions and transformations of these components. 4. Determine if the components that constitute the boundaries of the unity constitute those boundaries through preferential neighborhood relations and interactions between themselves, as determined by their properties in the space of their interactions. 5. Determine if the components of the boundaries of the unity are produced by the interactions of the components of the unity, either transformation of previously produced components, or by transformations and/or couplings of non-components elements that enter the unity through its boundaries. 6. If all the other components of the unity are also produced by the interactions of its components, as in 5, and if those, which are not produced by the interactions of other components, participate as necessary permanent constitutive components in the production of other components, you have an autopoietic unity.

Figure 1: Principles of autopoiesis (Varela et al., 1974).

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determine if a given entity is autopoietic, and also used to create a computer model of autopoiesis in a chemical scenario (McMullin, 2004).

1.2

Introduction to Knowledge Management and Organizational Learning

Knowledge management is a discipline that has emerged from the move toward a knowledge economy (Green, 2005) where knowledge is the key asset in an organization. Knowledge management, the culmination of many different disciplines, can be deﬁned as: the effective learning processes associated with exploration, exploitation and sharing of human knowledge (tacit and explicit) that use appropriate technology and cultural environments to enhance an organization’s intellectual capital and performance. (Jashapara, 2004)

Jashapara (2004) highlights the importance of capturing employees’ knowledge and successfully sharing it with other coworkers. Knowledge management is often split into three generations (Metaxiotis et al., 2005). The ﬁrst generation was concerned with deﬁning knowledge management, investigating possible systems, and looking at the beneﬁts of such systems. Advances in artiﬁcial intelligence also prompted study into how knowledge could be represented and stored. The second generation recognized the inﬂuence knowledge management could have in management information systems, for example, creating frameworks and instigating organizational change. The third, and current, generation is based on new insights and practices developed from the second generation. According to Wiig (2002), the third generation is more ‘‘integrated with an enterprise’s philosophy, strategy, goals, practices, systems, and procedures.’’ This is in recognition that knowledge management has links wider than information management. The third generation reﬂects the work of Ryle and Polanyi by emphasizing the link between knowing and action (Paraponaris, 2003). Often considered a part of knowledge management, organizational learning is a concept through which organizations aim to improve their performance through the coordinated learning of its members. Individual learning will play a role in organizational learning, but it is not sufﬁcient by itself. Organizational learning can be deﬁned as: the product of organizational members’ involvement in the interaction and sharing of experiences and knowledge. This shared form of knowledge is bigger than the simple added [sic] of the individuals’ learning capacities. (Curado, 2006)

This deﬁnition clearly emphasizes the importance of employees actively learning, as well as being free to explore by themselves. Curado (2006) appears to emphasize delegation of tasks and responsibility by encouraging people to achieve the results they want to achieve. In this way, Curado (2006) is keeping the emphasis of organizational learning ﬁrmly on individual learning. Organizational learning is comprised of three processes (Yeo, 2005): individual learning, team/group learning, and organizational learning. Individual learning

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focuses on activities that help the individual to learn, or solve problems on their own and is generally accepted as following the Lewinian experiential model (Kolb, 1984). The Lewinian model of learning is a four-stage cycle, which places great importance on experience. The cycle starts with a concrete experience, which gives an opportunity for observation and reﬂection. These observations and reﬂections are then formed into generalizations or theories, which, in the ﬁnal stage, are tested in new situations (Kolb, 1984). The emphasis of this cycle is that learning is a continual process based on a person’s experience and testing of abstract concepts they develop as a response. Based on experience, it would appear that Kolb’s philosophy is the guiding ethos in statements such as ‘‘learn by doing’’ and ‘‘practice makes perfect,’’ showing that there has to be some element of practical experience involved in learning. Team learning is when individuals ‘‘solve problems by drawing on the strengths of other members in a team’’ (Yeo, 2005). The third process, organizational learning, is somewhat different from the ﬁrst two processes, since the focus is on external resources. The main objective in organizational learning is to ‘‘develop new principles, positions, aims, roles, and identity in preparing the organization for the dynamic changes of the external environment’’ (Yeo, 2005). These three processes are important in organizational learning, since they show ﬁrstly that organizational learning occurs when individuals learn, and secondly that individuals are perhaps the most important feature in organizational learning. This means a direct link can be made from individual learning, through team learning, to organizational learning. For the purposes of this research, organizational learning is a concept taken to exist under the wider discipline of knowledge management.

2 Does an Autopoietic Foundation Already Exist for Knowledge Management? The shift toward the knowledge-based organization that occurred in the 1990s (Johanessen, Olaisen, & Olsen, 1999) instigated a paradigmatic shift in the workings and management of organizations. A focus on internal motivation, managing relationships, and widespread idea generation became important, and subsequently, knowledge management became increasingly important. However, it quickly became apparent that knowledge management was growing from several disciplines (Jashapara, 2004), and as such lacked its own theoretical foundation. The argument for applying autopoiesis to knowledge management has already been presented (Johanessen et al., 1999; Scholl et al., 2004), outlining the shift in organizational philosophy and management style, and the need for an interdisciplinary approach. However, a review of current applications of autopoiesis reveals some signiﬁcant variations in the approach used and results gained. Current applications of autopoiesis to knowledge management include creating an epistemology, treating organizations as ﬁrst-order autopoietic entities, and using organizational learning as an alternative, better suited approach.

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Paul Parboteeah et al. An Autopoietic Epistemology for Knowledge Management

Epistemological research in the ﬁeld of autopoiesis typically takes one of two paths: one, assuming knowledge is autopoietic itself and another, suggesting knowledge is an emergent property of second-order autopoietic systems. This difference can also be traced back to the debate concerning whether autopoiesis can exist outside the molecular domain. Authors proposing knowledge itself is autopoietic (Hall, 2005) believe that autopoiesis can be applied to conceptual and other physical domains and ultimately that knowledge is living. Authors proposing that knowledge is an emergent property (Abou-Zeid, 2007) believe that knowledge is embodied in the knower, and subsequently cannot be separated from them. Any activity in the ﬁeld of knowledge management should start from an autopoietic deﬁnition of knowledge (Limone & Bastias, 2006). The rationale being that since organizations are cognitive systems, and are third-order autopoietic systems, any knowledge management effort should entail a cognitive aspect. It is possible that knowledge is a biological entity (Hall, 2005), and as such inherits the characteristics of any biological being, typically a system and a control mechanism. In which case two types of knowledge would appear to exist: embodied and encoded (Hall, 2005). Embodied knowledge, also known as tacit knowledge, is that which the autopoietic system would normally gain through its activities. Encoded knowledge, or ‘‘control information,’’ is knowledge encoded into the systems structure, such that it is used for that system’s survival. Hall’s (2005) concept of control information seems to bear a striking resemblance to that of DNA. This appears to be a reappearance of the idea put forward by Luisi (2003) that autopoiesis provides the biologic for the operation of the autopoietic entity. An alternative view of autopoietic knowledge arises from the perspective that autopoiesis cannot exist outside of the molecular domain. This view proposes that knowledge is embodied in the knower, and cannot be stored, transferred, or externally manipulated (Abou-Zeid, 2007; Biggiero, 2007). Knowledge of this form is always private, and that only information or data can be stored, transferred, or manipulated. With this as an epistemological base, it becomes difﬁcult to see how knowledge can be managed. From this viewpoint, it would appear all that can be done is try and support people learning and acquiring knowledge by themselves. With this in mind, it is possible to create a knowledge management support system (Abou-Zeid, 2007). The design of a knowledge management support system should feature two parts: one for the actual system and one for the procedures of designing the system, or ‘‘metadesign.’’ Such an approach would ensure that the principles of autopoiesis were inherent in the design of the system. A less explored aspect of autopoietic knowledge is the notion that knowing is a process intertwined with the process of living. Knowing can be deﬁned as leading to ‘‘effective action, that is, operating effectively in the domain of existence of living beings’’ (Maturana & Varela, 1998). The essence of this deﬁnition is that knowledge is the key to effective action, and that perhaps through the process of living, and acting, that knowledge may be admitted. An option that does not appear to have been explored in the literature is whether observation of, and participation in, effective action leads to the admittance of knowledge, whatever the form of knowledge may be.

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However, trying to follow a line of research could result in numerous problems, such as trying to deﬁne effective action, trying to evaluate whether any knowledge had been admitted, and whether that knowledge was the correct knowledge. It would seem prudent to end with Biggiero’s (2007) statement that ‘‘explicit knowledge is an oxymoron.’’ In other words, all knowledge is embodied within the knower, and subsequently, knowledge management systems trying to directly manage knowledge will fail. The position taken in this research will be akin to Abou-Zeid’s (2007) that knowledge cannot be stored, manipulated, or transferred: it is embodied in the knower, along with Biggiero’s (2007) view that all knowledge is private and only data or information can be transferred. 2.2

Autopoiesis as an Organizational Epistemology

When considering the concept of organizational knowledge, it is important to consider that while people exist in the physical domain, organizations can only exist in the nonphysical domain (Kay & Cecez-Kecmanovic, 2003). This profound statement implies that organizations cannot have the same physical knowledge as people, if they are capable of knowing at all. Following autopoiesis theory, organizational knowledge could be deemed an emergent property of organizations, as observed by other systems (i.e., people). However, the autopoiesis and knowledge management literature does not venture on how organizational knowledge arises, or indeed what it is comprised of. Typical knowledge management literature tends to assume organizational knowledge resides in organizational documents, procedures, and other formal/informal documents. However, following the autopoietic view of knowledge, it would appear that such documents are merely data or information stores, with the ‘‘real’’ knowledge being stored by the potential knower. If it is assumed that organizations are second-order autopoietic entities, then the knowing capability in people (truly second-order autopoietic entities) can be compared to the knowing capability in organizations (the pseudo second-order autopoietic entity). According to autopoiesis, people admit knowledge whenever they observe effective action. This can be interpreted as meaning that knowledge always exists in a context, and is inextricably linked to action. Knowledge of the knower can then only be displayed through effective action, which may or may not be observed by another potential knower. Applying this line of thought to organizations becomes difﬁcult because it implies that the organization, as a whole, is capable of observing (which is not limited to seeing) actions. The organization must then have the ability to store the knowledge it gains in a suitable structure, which may be comparable to the brain. By this stage, it becomes obvious that the concept of organizational knowledge is not a tenable position, and that only truly second-order autopoietic entities can possess knowledge. 2.3

Knowledge Management in Autopoietic Entities

An autopoietic approach to knowledge management in organizations immediately raises the possibility that organizations themselves could be autopoietic (Maula,

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2000). It is possible to suggest that integrated ideas about an organization’s memory and senses can improve the organization’s ability to learn and adapt to its environment. However, Maula (2000) considers organizations ﬁrst-order autopoietic entities, on account that they are biological phenomena that constantly reproduce their own boundary and internal strategic components. Whilst organizations can be described as biological phenomena (being comprised of people, who are comprised of cells), it is very contentious to argue that they can reproduce their own boundaries, when it is generally unclear what the boundaries of an organization are. Whilst boundaries can be deﬁned as ‘‘a connecting and absorbing surface between the company and its environment’’ (Maula, 2000), it remains a very abstract concept, one that would be very difﬁcult to identify in real life. The concept of strategic components also remains an abstract concept. With regards to knowledge management, Maula (2000) proposes that the boundary elements of an organization allow knowledge to ﬂow to and from the organization, but with a ‘‘screening’’ process incurred. As a result, the organization will become more aligned with its environment. Once knowledge has entered the organization, the ‘‘memory function,’’ or strategic components, allows access to accumulated knowledge, which in turn maintains the organization’s effective operation. Returning to the analogy of the cell, the boundary elements correlate to the boundary of the cell whilst the strategic components of the organization relate to the cells internal metabolism. Whilst Maula (2000) has been successful in creating and applying the model to case studies, it remains to be seen whether such a view of organizations and knowledge management can be maintained when organizations are viewed as third-order autopoietic systems. Whilst it appears no research has been performed, it can be assumed that since third-order autopoietic entities do not have a ‘‘deﬁned’’ boundary, or internal metabolism, the model would collapse. Perhaps a new model could look at modeling the interaction between people as the ‘‘internal metabolism’’ and the society within which the people exist as the boundary to the system.

2.4

Designing Autopoietic Knowledge Management Systems

Once the conceptual hurdle of conceptually creating an autopoietic knowledge management system has been overcome, the next step is to design and implement the system. There are two main approaches: using autopoiesis as an application framework (Thannhuber et al., 2001) and using autopoiesis as a kernel theory (Abou-Zeid, 2007). Whilst both theories start to design knowledge management systems, with Abou-Zeid (2007) separating the product and process needed, they still fall short of actually creating and implementing one. As the creation of autopoietic systems moves closer to implementation, it is apparent that not all aspects of autopoiesis can be used (Thannhuber et al., 2001), which contrasts what was previously thought (Abou-Zeid, 2007). The autopoietic framework by Thannhuber et al. (2001) does not use all aspects of autopoiesis. Instead, key aspects, all of which are relevant to the system developed, are used. Concepts such as circularity and self-reproduction are used, whereas ideas

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such as the spontaneous emergence of autopoietic entities are not used. Whilst not using autopoiesis in its true sense, or even using autopoiesis in the context of second-/ third-order autopoiesis, the model proposed appears to work in its context. This could be because only aspects of autopoiesis needed are used, with the rest discarded. This resistance not to use all of autopoiesis just because it is possible has beneﬁted the model being developed. However, because only parts of autopoiesis have been used, and other parts of the model do not obey autopoiesis, the ﬁnal model should not be called autopoietic, as has been done (Thannhuber et al., 2001).

2.5

Autopoietic Organizational Learning as the Preferred Approach

At ﬁrst glance, the attraction of applying autopoiesis to organizational learning over knowledge management appears obvious. First, the task of deﬁning knowledge is no longer an issue, since individual learning theories are the focus of organizational learning, and not the nature of knowledge. Determining whether organizations are autopoietic or not is also an issue that does not need to be covered. This is because a focus on individual knowledge leads to the possibility of organizational knowledge, which invariably leads to the issue of whether organizations are autopoietic. Of course, this issue has been considered closed by some (Romesin, 2002) who claim that autopoiesis can only exist in the molecular domain, and that most other systems are second- or third-order autopoietic systems. This also raises another interesting research avenue: can autopoiesis be used to make the ‘‘jump’’ from individual learning to organizational learning? It is an issue that does not appear to have been covered in the literature. The ﬁrst application of autopoiesis to organizational learning was by Hall (2005), who aimed to provide a biological-based framework for how organizations operate, with a focus on knowledge and organizational learning. Hall (2005) partly achieved this aim by creating an autopoietically founded model of individual learning and linked it with a modiﬁed version of Popper’s three worlds. The main argument for Hall’s work (2005) was that organizations were emergent, autopoietic, and evolutionary in nature, and had learning as a core process within themselves. Hall (2005) is also correct to note that any knowledge management activity should start from an autopoietic-based understanding of knowledge. However, a fundamental error exists because Hall (2005) attempts to deﬁne organizations as ﬁrst-order autopoietic entities, using Varela et al.’s (1974) checklist for identifying autopoietic entities. However, Hall (2005) also discusses the use of cybernetics in organizational learning, as opposed to autopoiesis. This is interesting because autopoiesis appears to have been developed from cybernetics theory; however, neither cybernetics nor autopoiesis literature mentions the other domain. First-order cybernetics is concerned with the regulation of systems. The systems are generally considered passive, independent objects that can be controlled and changed. First-order cybernetics also assumes that such systems can be freely observed without interfering with the system. Regulation arises through communication and control. The system continually

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monitors itself through a feedback loop, which effectively deﬁnes the system as selfregulating. Second-order cybernetics arose when researchers began applying cybernetic principles to the role of the observer, recognizing that the very act of observing a system would change the system under observation. Autopoiesis is undoubtedly a second-order cybernetic theory, with its continual focus on the observer and its impact on the system under study. Maula (2006) deﬁnes the organization as a living system, and proposes a ‘‘living composition’’ as an enabling infrastructure. However, the underlying problem in Maula’s (2006) model is that it also assumes organizations are ﬁrst-order autopoietic entities, and subsequently falls into the problems described earlier. Maula also discusses two knowledge ﬂows: ‘‘sensing’’ and ‘‘memory’’ (2006). The problem of objectifying knowledge in this way assumes that knowledge takes on a form that it was not meant to, namely that it can exist outside the knower. While Maula (2006) does consider learning as a process, it is unfortunate that an assumption is made that organizations are autopoietic. Despite also using Varela et al.’s (1974) checklist for identifying autopoietic systems, the incorrect conclusion that organizations are autopoietic is used. In light of these weaknesses, the living composition model presented is still a good representation of organizations and how they might learn and adapt to new situations. Perhaps, the model could be redesigned such that it recognizes that only data and information can ﬂow between different people/entities, and that organizations are not ﬁrst-order autopoietic entities. It is also evident from the literature that research has not looked at applying the scientiﬁc principles of autopoiesis to a preexisting model of organizational learning. However, this approach falls into the trap of not starting from an autopoietic deﬁnition of knowledge. The result of this process would be a list of criteria for making an existing model of organizational learning, or even knowledge management, autopoietic. The models to which autopoiesis had been applied would then need testing to ensure the changes made a positive impact. No impact, or a negative impact, would obviously require a profound restructuring of the research. Jackson (2007) starts from the premise that current research within knowledge management is lacking a foundation, and is ﬁlled with lots of disagreements. However, after an introduction to autopoiesis, the research simply presents comparisons between autopoiesis and different aspects of knowledge management and organizational learning, arguing that autopoiesis in its entirety is too complicated to be useful in an organizational setting. However, the resulting metaphorical analysis ﬁnds that aspects of autopoiesis that used were far too simple to be applied to organizational learning. Whilst this paper did follow the social constructivist approach (Jackson, 2007), it failed to recognize that organizations could be viewed as cognitive systems, or third-order autopoietic systems, instead focusing solely on ﬁrst-order autopoiesis. Jackson (2007) does realize that a problem exists in viewing organizations as ﬁrst-order autopoietic entities because truly autopoietic entities are purposeless, and the same cannot be said for organizations. Viewing organizations as third-order autopoietic entities would have removed the boundary problems (Jackson, 2007) and allowed research to focus on how third-order autopoiesis can create a consensual domain, and allow for languaging to occur, and ultimately

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increasing knowledge sharing within the organization. With this in mind, it should be possible to develop the concept of organizational learning to include concepts such as embodied knowledge and enacted cognition.

2.6

Summary of Current Research

As identiﬁed by this overview, the application of autopoiesis to knowledge management and organizational learning is a relatively new notion, with lots of ideas in their infancy. This presents a wealth of opportunity for this research to proceed. Research was also reviewed which looked at deﬁning organizations as autopoietic, but this was not deemed appropriate given the research of Romesin (2002), which explained that the molecular domain was the only domain capable of supporting autopoietic entities. The implications of this mean organizations must be viewed as third-order autopoietic entities, and people as second-order autopoietic entities. While unappealing to some, this viewpoint is conducive to this research, since it supports the view that only people can ‘‘know,’’ and not organizations, and that individual learning can be explored in terms of emergence and adaptation. This section has also raised the notion that autopoiesis can be used to give existing models of knowledge management or organizational learning a scientiﬁc foundation. Such an approach would aim to validate existing models using autopoietic principles and then to test the new model to examine any extra beneﬁts the changes bring. Organizational learning is also popular because it is supportive of an autopoietic deﬁnition of knowledge. Organizational learning, and also individual learning, encourages people to think for themselves, innovate, and challenge any norms they might be accustomed to.

3 Using Autopoiesis as a Foundation for Knowledge Management and Organizational Learning 3.1

A Matching Methodology

At this stage, a suitable methodology is needed to apply autopoiesis to knowledge management. Research methodologies typically fall into two categories: positivism and interpretivism. Positivists believe that all knowledge arises from observing phenomena in a real and objective world (Cornford & Smithson, 1996). Favored with the science disciplines, positivist-based research aims to give hard, objective facts for results, which are easily repeatable. Repeatable results enable predictions to be made, assuming all variables remain the same or constant. Interpretivism, on the other hand, seeks to ‘‘understand reality through the realm of individual consciousness and subjectivity’’ (Jashapara, 2004). Such an approach recognizes that researchers affect the object they are researching, simply by researching it. Hence, perception becomes an important aspect to research, along with the realization that many different

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interpretations may exist for the same reality. As such, ‘‘understanding becomes a part of valid knowledge’’ (Cornford & Smithson, 1996). It is apparent that positivism or interpretivism neither solely suitable for applying autopoiesis to knowledge management. However, an integration of ideas from both perspectives would be ideal, and this is possible using matching. Matching is a new methodology developed by von Krogh, Roos, and Slocum (1996), and is used for the integration of two or more theories. Often described as unifying languages and relationships, matching is a two-step process: theoretical discourse and inscription. Theoretical discourse is the frequent dialogue about the theories, from which a new language emerges and through which the theories unite. Following on from which is inscription, which can be deﬁned as ‘‘the process of making and presenting knowledge from the ﬁrst stage, such that it can inform other theory building attempts’’ (von Krogh et al., 1996).

3.2

Applying the Principles of Autopoiesis

Numerous models could have been chosen for this research, such as Buckler (1996), Matthews (1999), or O¨rtenblad (2004), but Kim’s (1993) model of organizational learning was selected (Figure 2). While Buckler’s (1996) model does contain elements of feedback, it is not an inherently circular model, like that of Kim (1993). Buckler’s (1996) emphasis is also on organizational learning, virtually ignoring the process of individual learning. Matthews (1999), on the other hand, has undeniably created a circular model, but again does not detail how individual learning occurs, or indeed that it occurs in a separate cycle to organizational learning. O¨rtenblad (2004) uses the slightly different perspective of the learning organization, and has a circular model, but unlike Kim’s (1993) model, has two inputs to the cycle, which, in autopoietic terms, is undesirable, since external inﬂuences cannot determine change that occurs within an autopoietic entity. Kim’s (1993) model of organizational learning starts on the employee level, and deﬁnes individual learning as based on the experiential learning model. The cycle starts with a concrete experience, on which an observation may or may not be made. If an observation is made, then the individual will assess that observation (either consciously or subconsciously) to create, or design, generalizations or abstractions of that situation. Finally, the individual will test or implement the generalization in the real world, hence creating another experience and starting the cycle again. From individual learning, Kim (1993) adds the notions of single- and double-loop learning to the model. The main distinction made by Kim (1993) is that only double-loop learning uses memory, and single-loop learning links straight from the Observe– Assess–Design–Implement (OADI) cycle to individual action. The link to memory by double-loop learning infers the presence of mental models, which take to form of frameworks and routines. Together, these frameworks and routines model a person’s view of the world, in turn affecting any abstractions/generalizations they design and implement.

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Figure 2: Kim’s model of organizational learning (1993). Source: Massachusetts Institute of Technology. Copyright 2009. All rights reserved, distributed by Tribune Media Services.

Moving to the organizational level, Kim (1993) identiﬁes shared mental models as the key to access the organizational memory. Shared mental models can directly lead to organizational action, but can also affect individual action through a person’s individual mental models. These two leads for shared mental models are double-loop learning cycles because, as identiﬁed earlier, any use of a memory store infers doubleloop learning occurring. The model goes on to show how individual and organizational action occurs, with the resulting environmental response that is observed by the original OADI cycle at the start of the model. The matching process used to develop the model of knowledge in this paper took place over the course of several meetings between a Ph.D. student and the supervisory team. All potential terms to be used in the model were discussed and deﬁnitions of words were explored to resolve any conﬂicts, for instance, whether the term ‘‘observation’’ was purely related to sight, or all senses. The concept of environmental responses was also discussed, in light of the notion of perturbations in autopoietic

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entities. The issue of whether organizations could be autopoietic, and have a memory, or even knowledge, was discussed. Applications of the model were also explored to ensure terminology being used was not inherently restrictive. The second stage of the process involved modiﬁcation of the model. After the model was modiﬁed, it was subject to numerous reviews prior to being ﬁnalized.

3.3

The Autopoietic Model of Organizational Learning

As a result of the matching process, four changes were made to the original model of organizational learning. First, an extra ‘‘Observe’’ stage was added to the OADI loop, between the ‘‘Implement’’ and original ‘‘Observe’’ stages. This extra ‘‘Observe’’ stage also makes the OADI loop comparable to the cell wall–cell metabolism argument presented by Maturana and Varela (1998). They propose that neither can come ﬁrst in the production of the cell, but rather they must develop together to ensure the successful creation of a cell. The two ‘‘Observe’’ stages also recognize the different types of observation that need to occur. The ﬁrst is a general observation/problem detection process, whilst the ‘‘Observation’’ stage after the ‘‘Implement’’ stage is more a reﬂection process. The second change was to change ‘‘Environmental Response’’ to ‘‘Observed Environmental Response.’’ This is because the environment can never be part of the system it contains (Maturana & Varela, 1998). If the model kept the ‘‘Environmental Response,’’ then it would imply that the system is not distinct from the environment, breaking one of the autopoietic principles developed by Varela et al. (1974). The third change to the model was to rename ‘‘single-loop learning’’ as ‘‘autopoietic learning.’’ Single-loop learning can be deﬁned as learning that involves the detection and correction of error (Argyris & Scho¨n, 1996), which essentially means if an error occurs, the person changes their actions so the error does not happen again. One feature of autopoietic entities is that ‘‘the environment only triggers structural changes in the autopoietic unities (it does not specify or direct them)’’ (Maturana & Varela, 1998), and this is akin to single-loop learning because the consequences do not determine the change that will happen, it merely triggers it. This kind of learning can be renamed autopoietic learning, deﬁned as a random change of behavior when current actions do not have the desired effect or outcome. The key feature of autopoietic learning is that when an undesired outcome occurs, the person undertakes an unconscious attempt to produce the desired effect. This is characterized by a random change of behavior without any analysis of what went wrong in the ﬁrst instance. The ﬁnal change to the model was to rename ‘‘double-loop learning’’ as ‘‘allopoietic learning.’’ Double-loop learning is when the consequences of an action cause the person to look back at their ‘‘governing variable’’ (Smith, 2001), or ‘‘individual mental models’’ (Kim, 1993), and determine frameworks or routines that caused the consequences so that they can be changed. This is not similar to an autopoietic entity because change is not only being triggered, but also determined. Subsequently, if a machine is not autopoietic, it is an allopoietic machine which has,

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as a product of its functioning, something different from themselves (Maturana & Varela, 1980). Hence, double-loop learning can be considered allopoietic learning because when a person carries out double-loop learning, the product is a new mental model, or a model speciﬁed by the environment. Allopoietic learning can be deﬁned as a speciﬁc, and possibly, known change of behavior when current actions do not have the desired effect or outcome. It is also interesting to note the location of where the arrows indicating doubleloop learning leave and return to the OOADI cycle. With single-loop learning, the individual ‘‘sees’’ the result of their actions through the ‘‘Observe’’ stage of their learning. This observation then affects the implementation next time, and this has already been shown as being autopoietic. However, the difference comes with doubleloop learning. The arrow going to the Individual Mental Models box does not leave from one of the four processes in the OOADI cycle. This again shows that doubleloop learning cannot be autopoietic as it is an option that must be actively used by the individual. The ﬁnal model of autopoietic organizational learning is shown in Figure 3.

4 Testing the Autopoietic Model of Organizational Learning It would appear that the abstract nature of the model could hinder developing a suitable survey. Concepts such as mental models are by their very nature abstract, and trying to identify and evaluate them could prove very difﬁcult. It could be easier, and more useful, to look at the effect of the presence of mental models. For instance, instead of asking respondents about their own frameworks and routines, the questions could ask about the respondents’ personal experience and how often they use assumptions. The model is also circular, which itself is suited to autopoiesis. Testing the circular nature could mean the questions become self-checking. In other words, as questions develop around one part of the model, by the time they move around the model and end up in the same place, the questions should be testing what they were testing originally. The circular nature of the model could also cause problems because, if elements are related in a circular nature, it could make it difﬁcult for questions to make an entrance into the loop to start testing. However, an alternative approach to viewing the model as circular is to view the model as numerous authors’ works combined into one model. This would aid testing because each authors’ section could be independently tested. The very nature of organizational learning itself will undoubtedly have an impact on the method used to test the model. As already identiﬁed, the abstract nature of concepts involved means the survey will either have to make explicit what the concepts mean, potentially reducing their meaning or leave the respondent to interpret the concepts and risk their incorrect interpretation. A balance is clearly required; the survey would interpret the more abstract and vague concepts, while leaving the respondents to interpret the more well-known concepts.

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Figure 3: The autopoietic model of organizational learning. There is also a problem with the German word ‘‘Weltanschauung,’’ of which there is no direct English translation. In the original model, ‘‘Weltanschauung’’ is interpreted as the organization’s view of the world, or even just the organization’s view point (Kim, 1993). A problem then arises if a native German answers the survey, because they could understand the full extent of the term, and so provide an answer radically different from other respondents. The best approach would therefore be to put the interpretation of the word into the survey, and not allow the respondents to interpret ‘‘Weltanschauung’’ for themselves. Also when dealing with abstract concepts in surveys, it could be very easy to give away intended answers in the question itself. It would be better for the respondents to offer the answers themselves, which is why an interview, which allows respondents to elaborate when they feel necessary, is probably better.

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5 Conclusion This chapter has shown that autopoiesis can be adapted for use as an underlying paradigm for knowledge management in organizations. By using the principles developed by Varela et al. (1974), and following the matching methodology (von Krogh et al., 1996), a new model of organizational learning was developed. Further work is necessary to test the model and consider whether the model could more accurately reﬂect work scenarios. Of course, the model as it currently stands could be tested in several organizations to determine if the ﬁndings this chapter presents are reproducible. The research presented in this chapter would seem to suggest that autopoiesis could be used as a backdrop to knowledge management in organizations. In doing so, autopoiesis also appears to introduce a multidisciplinary approach to knowledge management by introducing biological thinking. Perhaps as an aside, it would be interesting to introduce Luhmann’s (1995) concept of social autopoiesis to add a social dimension to knowledge management. Either way, this chapter has provided an insight into how autopoiesis can beneﬁt knowledge management, and followed the notion through to developing a model ready for testing.

References Abou-Zeid, E. (2007). Towards a design theory of autopoietic knowledge management support systems. In: M. Khosrow-Pour (Ed.), Managing worldwide operations and communications with information technology (pp. 614–617). Vancouver, Canada: IGI Publishing. Argyris, C., & Scho¨n, D. (1996). Organizational learning II: Theory, method and practice. Reading, MA: Addison Wesley. Biggiero, L. (2007). Organizations as cognitive systems: What do they process and deliver? http:// Mpra.Ub.Uni-Muenchen.De/3137: Munich Personal Repee Archive. Buckler, B. (1996). A learning process model to achieve continuous improvement and innovation. The Learning Organization, 3(3), 31–39. Cornford, T., & Smithson, S. (1996). Project research in information systems: A student’s guide. Basingstoke: Macmillan Press. Curado, C. (2006). Organizational learning and organizational design. The Learning Organization, 13(1), 25–48. Green, A. (2005). A framework of intangible valuation areas. In: M. Stankosky (Ed.), Creating the discipline of knowledge management: The latest in university research (pp. 189–208). Amsterdam: Elsevier Butterworth Heinemann. Hall, W. P. (2005). Biological nature of knowledge in the learning organization. The Learning Organization, 12(2), 169–188. Hasan, H., & Gould, E. (2003). Activity-based knowledge management systems. Journal of Information & Knowledge Management, 2(2), 107–115. Holsapple, C., & Joshi, K. (1997). Knowledge management: A threefold framework. Kentucky Initiative for Knowledge Management, Paper no. 104. Jackson, T. W. (2007). Applying autopoiesis to knowledge management in organizations. Journal of Knowledge Management, 11(3), 78–91.

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Jashapara, A. (2004). Knowledge management: An integrated approach. Essex: Ft Prentice Hall. Johanessen, J., Olaisen, J., & Olsen, B. (1999). Systemic thinking as the philosophical foundation for knowledge management and organizational learning. Kybernetes, 28(1), 24–46. Kay, R., & Cecez-Kecmanovic, D. (2003). Organizational knowledge & autopoiesis: Towards a new view. The European Conference on Information Systems ECIS 2003 (CD-ROM), Naples, Italy. Kim, D. H. (1993). The link between individual and organizational learning. MIT Sloan Management Review, 35(1), 37–50. Kolb, D. A. (1984). Experience as the source of learning and development. Upper Saddle River, NJ: Prentice Hall. Liao, S. (2003). Knowledge management technologies and applications — Literature review from 1995 to 2002. Expert Systems with Applications, 25, 155–164. Limone, A., & Bastias, L. E. (2006). Autopoiesis and knowledge in the organization: Conceptual foundation for authentic knowledge management. Systems Research and Behavioral Science, 23(1), 39–49. Luhmann, N. (1995). Social systems. Palo Alto, CA: Stanford University Press. Luisi, P. L. (2003). Autopoiesis: A review and a reappraisal. Naturwissenschaften, 90(2), 49–59. Matthews, P. (1999). Workplace learning: Developing an holistic model. The Learning Organization, 6(1), 18–29. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis: The organization of the living. In: H. R. Maturana & F. J. Varela (Eds), Autopoiesis and cognition: The realization of the living (pp. 59–140). Dordrecht: D. Reidel Publishing Company. Maturana, H. R., & Varela, F. J. (1998). The tree of knowledge: The biological roots of human understanding (Revised ed.). Boston, MA and London: Shambhala. Maula, M. (2000). The senses and memory of a ﬁrm — Implications of autopoiesis theory for knowledge management. Journal of Knowledge Management, 4(2), 157–161. Maula, M. (2006). Organizations as learning systems: ‘‘Living composition’’ as an enabling infrastructure. Oxford: Elsevier. McMullin, B. (2004). Thirty years of computational autopoiesis: A review. Artiﬁcial Life, 10(3), 277–295. Metaxiotis, K., Ergazakis, K., & Psarras, J. (2005). Exploring the world of knowledge management: Agreements and disagreements in the academic/practitioner community. Journal of Knowledge Management, 9(2), 6–18. Mingers, J. (1995). Self-producing systems: Implications and applications of autopoiesis. New York, NY: Plenum Press. O¨rtenblad, A. (2004). The learning organization: Towards an integrated model. The Learning Organization, 11(2), 129–144. Paraponaris, C. (2003). Third generation R&D and strategies for knowledge management. Journal of Knowledge Management, 7(5), 96–106. Romesin, H. R. (2002). Autopoiesis, structural coupling and cognition: A history of these and other notions in the biology of cognition. Cybernetics and Human Knowing, 9(3/4), 5–34. Scholl, W., Konig, C., Meyer, B., & Heisig, P. (2004). The future of knowledge management: An international delphi study. Journal of Knowledge Management, 8(2), 19–35. Smith, M. K. (2001). Chris Argyris: Theories of action, double-loop learning and organizational learning. In: The encyclopedia of informal education. www.infed.org/ thinkers/argyris.htm (Accessed 12 April). Thannhuber, M., Tseng, M. M., & Bullinger, H. (2001). An autopoietic approach for building knowledge management systems in manufacturing enterprises. Annals of the CIRP, 50(1), 313–318.

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Varela, F. J., Maturana, H. R., & Uribe, R. (1974). Autopoiesis: The organization of living systems, its characterization and a model. Biosystems, 5(4), 187–196. von Krogh, G., Roos, J., & Slocum, K. (1996). An essay on corporate epistemology. In: G. von Krogh & J. Roos (Eds), Managing knowledge: Perspectives on cooperation and competition. London: Sage. Wiig, K. (2002). New generation knowledge management: What may we expect? Arlington, TX: Knowledge Research Institute. Yeo, R. K. (2005). Revisiting the roots of learning organization: A synthesis of the learning organization literature. The Learning Organization, 12(4), 368–382.

Chapter 15

Autonomous Cooperation — A Way to Implement Autopoietic Characteristics into Complex Adaptive Logistic Systems? Michael Hu¨lsmann, Bernd Scholz-Reiter, Philip Cordes, Linda Austerschulte, Christoph de Beer and Christine Wycisk

1 Introduction From a logistical perspective, a tendency from linear supply chains to international supply networks (ISN) can be observed (Surana, Kumara, Greaves, & Raghavan, 2005; Hu¨lsmann, Scholz-Reiter, Austerschulte, de Beer, & Grapp, 2007; Mason, 2007). This tendency includes changes of logistic system structures and environmental conditions. Modern supply network structures can be described as complex adaptive logistics systems (CALS) (Wycisk, McKelvey, & Hu¨lsmann, 2008). One key driver for increasing uncertainty and sensitivity to diverse and changing environmental requirements is the appearance of complexity and dynamics of CALS and their surrounding systems (Wycisk et al., 2008). The understanding of ISN as CALS leads to the realization that the inherent complexity of these systems causes unavoidably nonlinear behavior (Wycisk et al., 2008). In this context, a need to analyze opportunities, threads, and impacts for CALS caused by complexity and dynamics can be observed: researchers in this ﬁeld like Okino (1993) or Ueda (1993) develop such examining concepts for gaining more robustness. Robustness in logistics systems means that a system is stable even in cases of external changes and that it is able to react to changing environmental conditions. In the context of information systems, robustness means to keep the capacity to handle information at a level that enables a system to make rational decisions (Hu¨lsmann & Wycisk, 2005). Therefore, characteristics like ﬂexibility, emergence, and autonomy in logistic systems are needed in order to cope with timely challenges of complexity and dynamics, like the

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‘‘bullwhip effect’’ (Forrester, 1961; Lee, Padmanabhan, & Whang, 1997). The fact that several living systems that feature suchlike characteristics have been examined by using the concept of autopoiesis (Varela, 1979), as well as other applications like economic systems (Zeleny, 1997) or even war as an autopoietic system (Matuszek, 2007), leads to the question if the idea of autopoiesis can offer some contributions to an optimal design of logistics systems. Therefore, autopoiesis might, on the one hand, function as a metaphor to describe logistics systems and its characteristics, which though would include that there are insurmountable differences between them (Drosdowski, Mu¨ller, Scholze-Stubenrecht, & Wermke, 1985). This would reﬂect an insufﬁcient perspective on the reality of possible prospective technologies and today’s logistics research. According to Wycisk et al. (2008) (quod vide, Surana et al., 2005; Choi, Dooley, & Rungtusanatham, 2001; Pathak, Day, Nair, Sawaya, & Kristal, 2007), logistics systems can be described as complex adaptive systems (CAS), which include characteristics like the system’s self-creation and self-reference. This, in turn, has been factored explicitly into the autopoietic theory (Maturana & Varela, 1980) and applied to social systems by Luhmann (Luhmann, 1984; Krause, 2005). Therefore, the questions arise, on the other hand, if characteristics of autopoietic systems can be implemented in logistics systems, such as ISN, in order to cope with increasing complexity and dynamics and what instruments are necessary to do so. The theoretic instrument used in this paper, to show respective possibilities, is the concept of autonomous cooperation, which basically states the ability of a system to react to changes in the environment based on own decisions and means of the system’s single components. The wider concept of autonomous cooperation is self-organization, which, in turn, has its roots in different concepts, from which one is the autopoietic theory. Therefore, it can be said that autonomous cooperation reﬂects the idea of autopoiesis and self-organization (e.g., Maturana & Varela, 1980; Haken, 1973; Prigogine, 1969) and allows a system to create ordered structures autonomously (Manz & Sims, 1980). Therefore, the concept of autonomous cooperation will be applied to CALS in order to examine its contributions to implement autopoietic characteristics and therefore to increase the system’s ability to deal with complexity and dynamics. Figure 1 illustrates the coherences between the autopoietic theory and ISN, which will be examined in detail in the following sections. The question this paper is going to answer is: Is the concept of autonomous cooperation a reasonable way to implement autopoietic characteristics into logistics systems in order to enable them respectively to increase their ability to deal with complexity and dynamics in CALS like ISN? In order to examine this question, the paper is going to proceed as follows. Section 2 represents the tendency from linear supply chains to ISN. This will be used to reveal the increasing sensitivity of ISN due to external events. Section 3 introduces the concept of CALS and connects it with the properties of autopoietic systems. The underlying concept of CAS will be described to examine its essential characteristics. One of these properties is autonomous cooperation. Due to the assumption that autonomous cooperation is able to contribute to logistics systems’ robustness, it will undergo a deeper examination in the following section of this article. Based on these properties, the connection between CAS and ISN will be observed. Afterwards, the concept of autopoietic systems will be introduced and connected with the ﬁndings of the previous examination. In doing so, the autopoiesis concept will be applied to logistical problems whereas the focus will be

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Figure 1: Coherences between complex adaptive logistics systems (CALS) and autopoietic theory. on the autopoiesis’ possible contributions to the mentioned research question. Following that, the potential abilities of autonomous cooperation to improve the robustness of a CALS that behaves like an autopoietic system, in this case an ISN, will be shown. Section 4 comprises a simulation that aims at testing empirically the hypotheses on the causal interrelation between different methods of autonomous cooperation and their ability to manage complexity and dynamics of CALS. The simulation model that will be used is a discrete event simulation in the ﬁeld of production networks. Section 5 draws conclusions of our ﬁndings and gives an overview on future research requirements.

2 Tendency from Linear Supply Chains to International Supply Networks The competitive environment of actors involved in logistic processes is characterized by increasing complexity and dynamics (Hu¨lsmann & Berry, 2004). Beside others, phenomena like hyper-competition(D’Aveni & Gunther, 1994), hyper-turbulence (Monge, 1995), and hyper-linking (Tapscott, 1999) have been discussed as essential causes for these changes in the environment of logistic actors (Hu¨lsmann, Grapp, & Li, 2008). Hyper-linking connotes that the enterprises supply chains are increasingly interwoven with each other, which means that these actors are not only linked to

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their direct business partners but as well indirectly to other logistic agents in other supply systems, other countries, and other cultures (Tapscott, 1999; Hu¨lsmann, Scholz-Reiter, Freitag, Wycisk, & de Beer, 2006). This results from the simultaneous integration of one agent in different supply networks and the agent’s multidimensional interrelations in combination with the system’s immanent openness to other systems in its environment (Tapscott, 1999). Therefore, the originally phrased deﬁnition of supply chain management that integrates all of the company’s activities and that is based on Porters value chain (Porter, 1980, 1999) has to be ampliﬁed by a view that focuses on the networks lying behind and connecting these value chains with others (Hu¨lsmann & Grapp, 2005). Terming this a network instead of a chain is more precise due to its more wide meaning that includes not only material ﬂow and information ﬂow, but also their coordination between every single logistic actor in the network (Hu¨lsmann & Grapp, 2005). In other words, a tendency from linear supply chains to ISN can be observed. However, Surana et al. came to the same conclusion but integrate the two terms by deﬁning a supply chain as ‘‘a complex network with an overwhelming number of interactions and inter-dependencies among different entities, processes and resources’’ (Surana et al., 2005, p. 4235). In the course of globalization, the number of actors as system elements in logistics systems increases. On the one hand, this leads to an increasing quantity of information the information system of an ISN has to handle. Therefore, a necessity for an increasing information-processing capacity can be observed in ISN (Hu¨lsmann et al., 2007). On the other hand, the more actors are linked to each other, the more potential problem sources exist. Being linked to many actors in a network means being dependent on many actors, which are in turn accident sensitive to different external events. Furthermore, this means that the system’s conditions change in increasingly smaller intervals (Hu¨lsmann & Berry, 2004). Therefore, new problems can occur from different directions with which companies might have never had to deal before. This leads to an increasing number of potential problems and therefore to a higher sensitivity of the whole supply network. Resulting from this, the butterﬂy effect can appear, which states that minor changes in a complex system can lead to completely different conditions of the system in the future (Lorenz, 1972). In consequence, the management of ISN is increasingly confronted with the challenge to adapt to environmental changes.

3 International Supply Networks as Complex Adaptive Logistics Systems with Autopoietic Behavior 3.1

Description of Used Concepts and their Connection to International Supply Networks

3.1.1 Complex adaptive systems. Many kinds of systems, from natural to artiﬁcial, can be characterized as complex, for example, ecologies, social systems, or

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communication networks (Surana et al., 2005). Thereby, the term complexity does focus not only on the number of elements in the system, but particularly on the quantity of relationships between the elements and between the elements and the environment (Do¨rner, 2001; Malik, 2000). Due to that, the more elements exist and the more these elements are linked to each other in any kind of relationship, the more complex the regarded system is. A biological cell, for example, is a typical complex system that consists of many proteins that send signals to each other, though they have multiple relationships to other proteins in the cell (Holland, 2006). Furthermore, systems are increasingly confronted with environmental changes, which lead to increasingly dynamic circumstances in which the elements in a system operate and in which the system is situated (Hicks, Gullett, Phillips, & Slaughter, 1975; Hu¨lsmann & Berry, 2004). Dynamic occurs when involved elements change themselves or their relationships to other linked elements in the network (Hu¨lsmann et al., 2007). Therefore, dynamics can be described as ‘‘the rate of modiﬁcation of a system over a speciﬁc period of time’’ (Windt & Hu¨lsmann, 2007, p. 35). Due to the above-mentioned aspects, it can be assumed that a system’s ability to adapt to environmental changes by its own means has an increasing relevance. Furthermore, it can be assumed that the concept of CAS can provide this ability (e.g., Surana et al., 2005; Choi et al., 2001; Holland, 2002; Wycisk et al., 2008), which leads to the necessity to examine these systems and their properties more precisely. Its roots can be found, on the one hand, in biology where systems with living entities were analyzed (Gell-Mann, 2002) and, on the other hand, in the theories of complexity and chaos (Mason, 2007). These different approaches have in common that they all deal with complex systems containing and constituted by a large number of elements, which are linked to each other in a complex structure. (Mason, 2007; Gell-Mann, 2002). According to Wycisk et al. (2008), the following properties, mentioned before by Kauffman (1993) and Holland (2002), are essential for CAS and its elements as well as for their behavior: (1) Heterogeneity, (2) Interaction, (3) Autonomy, (4) Ability to learn, (5) Melting Zone, (6) Self-organization, and (7) Coevolution. The elements within a CAS in turn can be called agents which may represent, for example, an individual, a team, or an organization (Choi et al., 2001; Surana et al., 2005; Holland, 2002). First of all, the agents in a CAS are heterogeneous. This means that they distinguish themselves from each other through different properties, functions, and rules (Holland, 2002). In consequence, this results in differentiated behavior within the system because every agent follows ‘‘individual goals under different constraints and different action patterns’’ (Wycisk et al., 2008, p. 111). The agents act as well autonomously to a certain degree since their actions are not totally determined by other entities, for example, from a higher level in a hierarchy (Kauffman, 1993; Holland, 2002). This results from the existence of the single agents’ own rules concerning their behavior (Mason, 2007); therefore, they are able to make decisions autonomously without the need of any supervision; in other words, the system is characterized by decentralized decision-making (Windt & Hu¨lsmann, 2007;

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Probst, 1987). Decisions can be made decentrally if the system provides adequate single elements with necessary resources for it (e.g., relevant information). In contrast to totally hierarchical systems, elements in heterarchical structures have the authorization to make decisions about different action-alternatives autonomously without having to call an entity from an higher level in an hierarchical structure (Windt & Hu¨lsmann, 2007). That in turn means that the management of a system does not have to absorb the complexity that accompanies every single decision situation in the system. The internal and external complexity, which the management of a system has to absorb, decreases because it can be distributed among its multiple elements (Hu¨lsmann & Grapp, 2005; Hu¨lsmann & Wycisk, 2005). Furthermore, autonomy implies the system’s ability to arrange its structure by own means (selfformation), to supervise itself (self-control), and to develop without any impact from the system’s environment (self-development) (Probst, 1987). However, these mentioned characteristics lead as well to a nonpredictability about the system’s future states, though it is nondeterministic (Fla¨mig, 1998). This aspect follows the characteristics autonomy — in logical consequence and therewith the decentralized decision-making, which are the reasons for the nonpredictability of the single elements’ behavior. This means that multiple possible future states of the system exist (Haken, 1983). Due to the agents’ heterogeneity, the agents are well equipped with heterogeneous resources — for example, information — which motivates them to engage in exchanges among each other. Therefore, CAS are characterized by interactions between agents as the system’s elements (Holland, 2002, 2006; Wycisk et al., 2008). In order to do so, agents have to communicate with each other, which in turn means that agents react on other agents’ actions. In the course of a direct exchange of information between them, they do not have to put up with a detour over a hierarchical higher entity. In consequence, the necessary time for decision-making processes abbreviates due to the elements’ possibilities to communicate directly with each other (Hu¨lsmann et al., 2008). Insofar this can lead to synergetic effects which in turn can result in reaching a qualitative higher level of the whole system (Haken, 1983). Finally, a CAS is characterized by its agents’ ability to learn (Holland, 2002; McKelvey, Wycisk, & Hu¨lsmann, 2009). According to Holland, elements ‘‘y modify their rules as experience accumulates, searching for improvements’’ (Holland, 2002, p. 25). Thereby CAS achieve the possibility to react on environmental changes, in other words the ability to adopt. In consequence, CAS can be regarded as intelligent systems, whereas the intelligence ‘‘y may be located in its smart parts (y) and their connectivity’’ (McKelvey et al., 2009, p. 7). Besides the properties of CAS, there is a need to examine its behavior (Wycisk et al., 2008). First of all, it has coevolutionary characteristics resulting from the agents’ autonomy — and interaction — properties as well as their ability to learn. On the one hand, agents respond to other agents’ actions; on the other hand, the system is capable of reacting to environmental changes and in turn can shape its environment through actions or responses, which have inﬂuences on it (Kauffman, 1993; Choi et al., 2001).

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Furthermore, a CAS is located in a so called ‘‘melting zone,’’ which means that it is neither completely ordered nor completely chaotic; rather it is located in somewhere between these extremes (Kauffman, 1993, Surana et al., 2005). According to Surana et al., the elements ‘‘exist in quasi-equilibrium and show a combination of regularity and randomness’’ (Surana et al., 2005). This implies that different locations within this melting zone could be attended by different degrees of contributions to the system (e.g., to the robustness or the ﬂexibility of the system), which in turn means that there could be an optimal location that has to be identiﬁed. Finally, the behavior of a CAS and its agents is characterized by self-organization (e.g., Holland, 1995; Choi et al., 2001; Surana et al., 2005), which describes an organization principle that a system uses for its own conﬁguration and organization (Bea & Go¨bel, 1999; Probst, 1987). In reference to Windt and Hu¨lsmann (2007) respectively Grapp, Wycisk, Dursun, and Hu¨lsmann (2005), its roots can be found, as shown in Figure 2, in the following six primal concepts from different disciplines: Synergetics (Haken, 1973), Dissipative Structures (Prigogine, 1969), Cybernetics (von Foerster, 1960), Chaos Theory (e.g., Lorenz, 1963; Mandelbrot, 1977), Ecosystems (e.g., Bick, 1973), and the concept of Autopoiesis, which will be introduced in detail later on (Maturana & Varela, 1980). Self-organization can be regarded as a part of management and describes the modality how an ordered structure in a system can arise out of itself, which includes the ability to arrange its own processes and structures without any impact from the outside of the system (Bea & Go¨bel, 1999; Probst, 1992; Mainzer, 1994; Windt & Hu¨lsmann, 2007). According to Probst (1987) self-organizing systems are characterized by the following four properties: (1) Complexity, (2) Autonomy, (3) Redundancy, and

Figure 2: Primal concepts of the idea of self-organization. Source: Windt and Hu¨lsmann (2007), with kind permission of Springer Science and Business Media.

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(4) Self-reference. The terms complexity and autonomy were described above and constitute essential characteristics of CAS. The existence of autonomously acting agents, which interact with each other and whose behavior is not determined by other entities, enables a self-organizing system to evolve by own means (Windt & Hu¨lsmann, 2007), which in turn leads to unpredictability of future system states (Haken, 1987; Prigogine, 1996). Redundancy means that there is no difference between organization and execution in self-organizing systems (Probst, 1987). Furthermore, the elements are equipped with similar or the same assets and abilities, which means that no functions exist in the system that can be executed by just one element (Wycisk, 2006). Insofar, the system comprises no elements with a permanent dominant impact on the system’s development; in other words, the elements have similar degrees of inﬂuence on it (Probst & Mercier, 1992). This aspect shapes the system’s heterarchical structure (Hu¨lsmann & Wycisk, 2006). This leads to an increasing ﬂexibility of the system because if one element is for any reason no longer able to execute its function, this function can be taken over by another element. Beside this and as mentioned before, the complexity the management of a system has to absorb decreases due to the absence of an entity on an higher level in an hierarchical structure that supervises the single elements’ functions (Hu¨lsmann et al., 2008). Whereas self-organizing systems are, on the one hand, open to absorb information and resources, which enables them to adapt to environmental changes (Varela, 1979; Malik, 2000), on the other hand, they are operationally closed (Windt & Hu¨lsmann, 2007). This results from their self-reference, which describes the system’s ability to build its own borders by own means. It offers the elements the information they need to decide autonomously, which is the basis for their actions. Therefore, the system’s behavior starts with the existence of this characteristic, as well as the ability to measure environmental changes or to realize possibilities for internal synergies (Probst, 1992). In consequence, self-reference enables the system to distinguish itself from its environment (Luhmann, 1984).

3.1.2 Autonomous cooperation. The concept of self-organization, which is, as shown above, an important property of CAS, leads to an approach that is currently discussed as a possible enabler for coping with increasing complexity and dynamics in the system’s relevant environment: autonomous cooperation (Hu¨lsmann et al., 2006, 2007). The concept of autonomous cooperation is part of a multilevel perception that is based on the ideas of complexity sciences (Hu¨lsmann & Wycisk, 2005). Although a clear assignment has not been found in science up to now, it can be distinguished between self-management, self-organization, and autonomous cooperation (Hu¨lsmann & Wycisk, 2005; Wycisk, 2006). The widest notion is constituted by the term self-management, which describes the whole system in its ability to conﬁgure itself independently from other systems (Manz & Sims, 1980). As mentioned before, self-organization has to be understood as a part of management, more precisely selfmanagement (Hu¨lsmann & Wycisk, 2005).

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Figure 3: Classiﬁcation of the terms ‘‘self-management,’’ ‘‘self-organization,’’ and ‘‘autonomous cooperation.’’ Source: Hu¨lsmann and Wycisk (2005). The concept of autonomous cooperation focuses on the single system-elements. Because it is the most narrow view of self-organizing systems, as shown in Figure 3, it can be seen as a part of the larger concept self-organization, which in turn has part of its roots in the concept of autopoiesis. Its main goal is to achieve a better ability to cope with increasing complexity and dynamics, though to increase the robustness of a system (Windt & Hu¨lsmann, 2007). According to Windt and Hu¨lsmann, autonomous cooperation describes ‘‘[y] processes of decentralized decision-making in heterarchical structures. It presumes interacting elements in non-deterministic systems, which possess the capability and possibility to render decisions independently’’ (Windt & Hu¨lsmann, 2007, p. 8). Resulting from this deﬁnition, processes in CAS can be called autonomously cooperating when the following ﬁve constitutive attributes, which were already described above, can be observed: (1) Decentralized decision-making, (2) Autonomy, (3) Nondeterminism, (4) Heterarchy, and (5) Interaction (Windt & Hu¨lsmann, 2007). As shown in Figure 4, these constitutive attributes of autonomous cooperating processes can adopt different degrees (e.g., within a continuum between 0 and 100%) on different levels (e.g., decision system, information system, and execution system), which can vary in the course of time (Hu¨lsmann & Grapp, 2006; Hu¨lsmann et al., 2008). 3.1.3 Complex adaptive logistics systems. As shown before, a tendency from linear supply chains to ISN can be observed (Surana et al., 2005, Hu¨lsmann et al., 2007; Mason, 2007). Several authors were arguing that ISN in turn can be regarded as CAS

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Figure 4: Polarization graph. Source: Hu¨lsmann and Grapp (2006).

(e.g., Surana et al., 2005; Choi et al., 2001; Pathak et al., 2007; Wycisk et al., 2008), which leads to the term CALS. According to Pathak et al., it is self-evident to identify an ISN as a CAS because ‘‘organizations exhibit adaptivity and can exist in a complex environment with a myriad relationships and interactions’’ (Pathak et al., 2007, p. 550). Complexity in ISN results from the ‘‘large amount of involved organizations and relations between these organizations,’’ which can be circumscribed by the already mentioned term hyper-linking (Hu¨lsmann et al., 2007). To illustrate this complexity, the example of a multinational textile company in Hong Kong will be regarded. In a highly globalized world, the value chains of companies are not limited to one country, not to mention to one organization. This company can have its customers, its different retailers, as well as its different production locations and suppliers for different parts of their clothes widespread over the world (Natarajan, 1999). This leads to the system’s inherent complexity and requires, therefore, an ability to cope with it. Furthermore, increasing dynamics in ISN can be observed, which is a result of the already mentioned phenomena hyper-linking (Tapscott, 1999), hyper-competition (D’Aveni & Gunther, 1994), and hyper-turbulence (Monge, 1995). Dynamics emerge when, for example, the contracts with retailers or with extern suppliers expire or organizations linked to the exempliﬁed textile company are not able anymore for any reason to deliver resources. The textile company can as well search for better or cheaper possibilities to get the needed parts like yarn or zippers or for better and cheaper ways to deliver the clothes to the retailers as well as to search for new retailers

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and in new markets. However, changes in the ISN can arise from multiple different directions, which reveals the inherent dynamic in ISN. If organizations do not adapt to these kind of changes, they can lock-in into a suboptimal situation in which they are no longer able to respond adequately to the resources requests of their environment, which in turn can lead to negative effects on the continuity of the organization (Schreyo¨gg, Sydow, & Koch, 2003). In consequence, this leads to the organizations’ necessity to adapt to environmental changes, which means to adopt the ability to act ﬂexible without losing stability, though to reach a balance (Hu¨lsmann et al., 2008). Surana et al. (2005) call a supply network ‘‘[y] highly non-linear, [that] shows complex multi-scale behavior, has a structure spanning several scales, and evolves and self-organizes through a complex interplay of its structure and function’’ (Surana et al., 2005). Therewith, the most essential CAS properties can be found in ISN. In the course of describing these properties, Pathak et al. (2007) as well as Wycisk et al. (2008) conﬁrm the parallels to supply networks but formulated a few limitations to them, for example, that CAS properties best ﬁt to living systems whereas logistics systems are only partly alive, for example, managers (Wycisk et al., 2008). But the validity of this limitation is decreasing due to the current development of new technologies that enable nonliving parts of systems to decide autonomously and though to approximate to the acting rules of human parts in the system and become a kind of intelligent. According to Scholz-Reiter, Windt, and Freitag (2004), a paradigm shift in supply networks can be observed that leads from centralized planning and control of nonintelligent entities to decentralized planning and control of intelligent entities in the network. In consequence, with consideration of these developments, ISN can be regarded as CALS (Wycisk et al., 2008; Pathak et al., 2007).

3.2

Autopoiesis and its Connection to Complex Adaptive Logistics Systems

3.2.1 Autopoiesis as a Theoretical Basis. The properties of CALS connected with the previously mentioned technological developments lead to the assumption that CALS can, in analogy to the biological roots of CAS, be regarded as living systems at least to a certain degree. Maturana and Varela (1980) dealt in their autopoiesis concept, which in turn is one of the primal concepts the self-organization approach is based on, with the question by what living systems can be distinguished from nonliving systems. Therefore, autopoiesis could be an adequate theoretical basis for further and deeper examination of the effects emanating from CALS and its implications for its management. The term autopoiesis derives from the Greek words autos ¼ self and poiein ¼ making respectively creation or production; therefore, it can be circumscribed as well with the terms self-production or self-creation (Maturana & Varela, 1980; Drosdowski, 1989). This concept was originally created to explain phenomena in biology and describes a system that is characterized by a network of the single elements within production processes, in which they interact with each other and create as well as realize the system itself in a recursive way (Maturana & Varela,

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1980). Furthermore, the single elements are distinct; thus, they are heterogeneous, and they are able to act without the need of conﬁrming it by another entity, which means that they are autonomous (Maturana & Varela, 1980) and that they act according to a set of speciﬁc behavioral rules (Zeleny, 1997, 2001). The autonomy of the elements within a system results from its circularity which means that there is no central reference point in the system from which it develops. According to Fla¨mig (1998), this is a central aspect in the autopoiesis concept. Therewith, the system’s elements build the network of production processes themselves; in other words, they constitute it and simultaneously build its borders. The organization of such a system is called an autopoietic organization, in other words, a system that organizes itself by its own means (Maturana & Varela, 1980; Maturana, 1999). Beside other authors that transferred this approach to new areas of application (Fla¨mig, 1998 and Luhmann (1984) uses it for certain questions in social sciences, which have relevance for the examination of organizations. The recursive selfproduction process is illustrated by communication between single actors in a closed social system (e.g., an enterprise). Therewith, communication functions as the creator of social systems. According to Luhmann (2006), communication does only exist as social systems and in social systems. Due to the impossibility to divide communication, a smaller element does not exist in a social system. Luhmann (1990) describes a chain effect in which every communication produces another following communication without the need of an external entity to trigger the following one; therewith this can be called a self-organizing system. 3.2.2 Complex Adaptive Logistics Systemsas autopoietic systems. Compared with the further described essential properties of CALS, autopoietic organizations show direct similarities. First of all and as shown above, CALS are self-organizing (Surana et al., 2005; Choi et al., 2001; Wycisk et al., 2008), as autopoietic systems (Maturana & Varela, 1980). This implies that autopoietic systems are characterized by the above-described properties complexity, redundancy, self-reference as well as by autonomy (Windt & Hu¨lsmann, 2007), which is an essential precondition of autopoiesis (Maturana & Varela, 1980). Therewith, just like in autopoietic organizations, no central entity exists in CALS that coordinates the single elements’ actions; therefore, a circularity can be observed (Fla¨mig, 1998). This in turn is a result of the system’s inherent characteristic of decentralized decision-making (Windt & Hu¨lsmann, 2007; Probst, 1987). Furthermore, the system’s elements are heterogeneous because their behavior is determined by different rules (Holland, 2002). A basic necessity for the functionality of a CALS as well as of an autopoietic system is the elements’ ability to interact with each other (Maturana & Varela, 1980; Holland, 2002, 2006; Wycisk et al., 2008), which means they exchange information by means of using a certain language (Maturana & Varela, 1980); in other words, they communicate (Luhmann, 2006). Because these interactions can result in actions that have inﬂuences on the system’s structure, which means the elements react to each other, they contribute in a recursive way to the creation of the system structures in which the interacting elements exist (Maturana & Varela, 1980). Therefore, the structure of the CALS as an autopoietic

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system evolves, develops, and is being controlled by its own elements, just like a biological cell that creates its own elements by own means (Fla¨mig, 1998); in other words, it is characterized by its recursive self-production. These properties lead in logical consequence to the elements’ ability to learn and to coevolution (Kauffman, 1993; Choi et al., 2001). When environmental changes occur, the single elements have to change their rules to keep the system alive due to the absence of an outside impact on the system. This means that the elements in an autopoietic system as well as in a CALS react to the other elements’ actions and to environmental changes. Changes of the elements lead in turn to changes in the system’s structures, whereas some of these changes can have inﬂuences on the environment of the autopoietic system (Kauffman, 1993; Choi et al., 2001). Finally autopoietic systems must have in analogy to CALS a kind of melting zone due to a system’s impossibility to be completely self-organized as well as being completely organized by an external entity; rather it is situated in a continuum between these two extremity pools (Wycisk, 2006). In summary, it can be said that CALS exhibits the same essential properties as autopoietic systems, which in consequence means that they can be regarded as one of that ilk.

3.3

Contributions of Autonomous Cooperation for Coping with External Complexity and Dynamics

As shown above, CALS (e.g., ISN) can be regarded as autopoietic systems, whereas these systems can comprise multiple organizations (e.g., production locations, retailers, suppliers) with multiple elements (e.g., managers, nonliving items equipped with technology that enables them to render decisions autonomously). Due to their self-organizing property, which is an essential characteristic of both concepts, they are characterized by their possibility to adopt autonomously cooperating processes (Windt & Hu¨lsmann, 2007). As mentioned before, the environment of CALS is characterized by increasing complexity and dynamics (Wycisk et al., 2008). These are in turn causes for external risks (Hu¨lsmann et al., 2007), which can be described as an impossibility to forecast future developments. This impossibility in turn has its causes in a lack of information, which would be necessary to decide under secure circumstances (Rosenkranz & Missler-Behr, 2005). Caused by increasing complexity and dynamics, the informational basis for secure decision-making is deteriorating (Hu¨lsmann et al., 2007). If the system is no longer able to handle the incoming information, which means that it is not able to make rational decisions, the system can become locked-in (Schreyo¨gg et al., 2003; Hu¨lsmann & Wycisk, 2005). This leads to a necessity to enlarge the capacity to handle the information the system is confronted with (Hu¨lsmann et al., 2007). The ability to cope with these external risks implies the system’s ability to adapt to environmental changes that are in turn causative for the appearance of external dynamics (Hu¨lsmann et al., 2007).

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Figure 5: Possible contributions of autonomous cooperation to a system’s ability to cope with external risks.

Autonomous cooperation has been discussed as a possible enabler for coping with these challenges and therewith to increase the system’s robustness (Hu¨lsmann et al., 2006). Hence, the question arises whether the inherent characteristics of autonomous cooperation can contribute to that. These possible contributions, shown in Figure 5, are described in the following text and will be illustrated by the already mentioned CALS example of a multinational textile company, which behaves like an autopoietic system. Decentralized decision-making: Due to the delegation of decisions to the single elements of the system (e.g., production locations), the decision-making capacity of the whole ISN increases (Hu¨lsmann et al., 2007). In consequence, the logistics system’s ability to handle information and therefore to adapt to environmental changes can be enlarged. For example, if one location of the textile company stops its production, a decision must be made which other location has to continue this work. If the headquarter would have to ask ﬁrst all locations if they are able to do the same work and then to ﬁnd the best alternative between all these locations, the decision-making process could take longer than in a case where every location stays in direct contact to the others and is able to render this decision independently. Therefore, the headquarters can be disburdened from its necessity to handle all information about the single system’s elements and the decision-making capacity of the whole ISN can be enlarged. Autonomy: If the elements can decide on their own, they are, as shown above, autonomous (Windt & Hu¨lsmann, 2007; Probst, 1987), which means that the single elements are responsible for the system’s design in which they exist. For this reason, the development of the system and therewith its direction is as well controlled by its

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single elements (Probst, 1987). This can lead to a superior system structure concerning its ability to absorb complexity and to handle dynamics, in other words, to adapt to environmental changes (Hu¨lsmann et al., 2007). For example, if the production locations can choose by own means what kind of clothes they produce and to which retailers in which countries they deliver their products, without having to ask the headquarter ﬁrst, the headquarter would have less decisions to make and therefore less complexity to absorb. Furthermore, if the elements in a logistics system are able to decide, the production locations can as well decide on their own to open up another one (e.g., to increase the production capacity) or to close one of them. Consequently, the production- and delivery-structure of the whole ISN would evolve and develop as well as if being controlled by its own production locations (Fla¨mig, 1998; Probst, 1987). This can lead to a superior production structure (which location produces which product and which should be downsized respectively enlarged), concerning its ability to handle dynamics caused by environmental changes (e.g., changes in the demand structure), compared to a structure that is totally controlled by a headquarter. In this aspect, the resulting recursive self-production of the system through its elements becomes apparent (Maturana & Varela, 1980). Interaction: Because the elements in the autopoietic CALS are able to interact respectively to communicate directly with each other (Holland, 2002, 2006; Wycisk et al., 2008; Maturana & Varela, 1980; Luhmann, 2006), a more target-oriented exchange of information can result. The elements exchange only their needed portion of information, so that they need less capacity to handle them (Hu¨lsmann et al., 2007). Picking up the mentioned example, the single production location that stays in direct contact with the other locations does not have to pick up a detour over the headquarter to get some needed information. Instead, they can directly ask other elements in the ISN (e.g., retailers or other production locations). Heterarchy: The heterarchic characteristic of an autopoietic CALS is constituted by independence between the elements and a planning unit. This might lead to a more complex and dynamic system structure and therewith to more internal complexity and dynamics the system has to cope with (Hu¨lsmann et al., 2007). Beside this effect, it also has to be taken into account that heterarchy can as well increase its ability to adapt to environmental changes. Because the single elements are equipped with similar resources and abilities, one element can undertake another element’s function. Therefore, the system’s ﬂexibility increases (Hu¨lsmann et al., 2008), which enables an organization to respond to changing environmental conditions (Sanchez, 1993). This can be illustrated as well by the already taken example of the textile company: If one of its contractors (e.g., a production location that produces a special kind of trousers) had a production stop due to any reason, the company could simply switch to another one that has the same assets and abilities to produce this kind of clothes. Nondeterminism: Finally, autopoietic CALS can reach a higher efﬁciency in dealing with complexity due to its nondeterminism (Windt & Hu¨lsmann, 2007). This results from the fact that future system states, which the elements have to comply with even when environmental changes occur, are not ﬁxed beforehand (Fla¨mig, 1998). Hence,

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uncertainty about the question whether the system will be able to handle environmental changes and enable it to react to changes in the system structure can be reduced (Hu¨lsmann et al., 2007). This implies an increasing ability to cope with risks resulting from dynamics. In the mentioned example of a textile company, nondeterminism would exist when the production locations, for example, were not bounded to a certain production plan for a ﬁxed time period. Changes in demands on the market could not be taken into account if they were bounded. Therefore, this nondeterminism enables the elements to change plans and the system to react to environmental changes, which can occur as risks for the whole ISN.

4 Empirical Test 4.1

Test Design

In the previous research, it has been identiﬁed theoretically that CALS can be seen as autopoietic systems because the main characteristics of both are similar or even identical. According to this, an empirical test of the implications that autonomous cooperation has on the management of CALS could allow to draw conclusions about autonomous cooperation in autopoietic systems as well. To measure the effects of autonomous cooperation on CALS, a simulation and measurement system has been developed that allows analyzing the effects of different autonomous cooperation methods on the robustness of production networks with different levels of complexity and different levels of external dynamics. In earlier work of the authors, the simulation model has been used to analyze effects of autonomous cooperation on ISN (Hu¨lsmann et al., 2006, 2007). A similar approach will be used to analyze effects of autonomous cooperation on CALS. Figure 6 shows the simulation model that has been implemented as a discrete event simulation. The scenario shows a matrix-like network of different production stages that are interlinked and that are able to exchange information, resources, and orders to perform a multistage production process. On one stage, the facilities are able to perform resembling production steps. Each order has a speciﬁc processing plan, i.e., a list of processing steps that have to be undertaken to produce goods. In the model, the orders are not directed by a centralized control entity but have the ability to render decisions on their next processing step autonomously by using different concepts of autonomous cooperation. Depending on the different autonomous control methods, the overall system shows altered behavior and dynamics. This model comprises the opportunity to evaluate the system’s ability to cope with different levels of complexity as well as different amounts of external dynamics. The complexity can be varied by using different numbers of production facilities or different kinds of orders and products. The orders enter the system at the sources. Here, the external dynamics can be varied by using different functions that deﬁne the arrival rate of different kinds of orders. By implementing different autonomous control methods, the ability of autonomous cooperation to inﬂuence a system’s

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International Supply Network

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Figure 6: Matrix-model of an ISN as an example for CALS. Source: According to Hu¨lsmann et al. (2007). robustness can be analyzed. Therefore, the system’s performance will be measured for different autonomous control methods with varying levels of complexity and external dynamics.

4.2

Methods of Autonomous Control

In the following, the applied autonomous control methods will be described. The ﬁrst method, called queue length estimator (QUE), compares the current buffer level at all parallel processing units that are able to perform the next production step. The buffer content is not counted in number of parts but the parts are rated in estimated processing time and the actual buffer levels are calculated as the sum of the estimated processing time on the respective machine. When a part has to render the decision about its next processing step, it compares the current buffer level, i.e., the estimated waiting time until processing, and chooses the buffer with the shortest waiting time (Scholz-Reiter, Freitag, de Beer, & Jagalski, 2005). The pheromone method (PHE) does not use information about estimated waiting time, i.e., information about future events, but uses data from past events. This method is inspired by the behavior of foraging ants that leave a pheromone trail on their way to the food. Following ants use the pheromone trail with the highest

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concentration of pheromone to ﬁnd the shortest path to food. In the simulation this behavior is imitated in a way that whenever a good leaves a processing unit, i.e., after a processing step is accomplished, the good leaves information about the duration of processing and waiting time at the respective processing unit. The following parts use the data stored at the machine to render the decision about the next production step. The parts compare the mean throughput times from parts of the same type and choose the machine with the lowest mean duration of waiting and processing. The amounts of data sets that are stored deﬁne the up-to-datedness of the information. This number of data sets can be used to tune the pheromone method. The replacement of older data sets resembles the evaporation of the pheromone in reality (Scholz-Reiter, Freitag, de Beer, & Jagalski, 2006). The due-date method (DUE) is a two-step method. When the parts leave a processing unit they use the queue length estimator to choose the subsequent processing unit with the lowest buffer level. The second step is performed by the processing units. The due dates of the parts within the buffer are compared and the part with the most urgent due date is chosen to be the next product to be processed (Scholz-Reiter, Freitag, de Beer, & Jagalski, 2007). The following simulation analyzes the overall system’s ability to cope with rising structural complexity and rising external dynamics using different autonomous control methods. At each source, the arrival rate is set as a periodically ﬂuctuating function. The logistical goal achievement is measured using the key ﬁgure throughput time for different levels of complexity and different autonomous control methods. Therefore, the simulation model is able to represent the main characteristics of CALS. The agents within the model have different characteristics, for example, different due dates or different production steps; therefore, they can be assumed to be heterogeneous. Additionally, interaction between the system’s elements is implemented, due to the agents’ ability to communicate with each other (e.g., the different orders communicate with the processing facilities; furthermore, the order agents communicate with each other using the stigmergy concept, i.e., communication via the environment by leaving information for following agents). As well the agents are able to act autonomously since the orders are able to render their decisions concerning the next processing step. Up to now the learning ability within the simulation model is limited due to the fact that the agents are modeled relatively simple so that they themselves do not have any learning abilities. In contradiction to that, the methods of autonomous control enable the system to react ﬂexibly to changes and to learn about changes in the system’s structure; for example, if a machine breaks down the control methods enable the agents to avoid this machine in the production plan and therefore enable the overall system to learn how to react on unexpected changes. The melting zone is part of the analytical aim of the simulation. As different grades of autonomous control are compared to each other via different control methods, they can be seen as different grades in a continuum between 100% decentralized decision-making and 100% centralized decision-making (Hu¨lsmann & Grapp, 2006). Therefore, the different methods of autonomous control might represent different locations in the melting zone. Finally, self-organization is implemented into the simulation model by applying the ability to the agents to organize their processing autonomously.

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Test Results

Figure 7 shows the results, i.e., the mean throughput times for the three different autonomous control methods in dependence of the system’s complexity. To the right of the ﬁgure, the system’s complexity is increased by enlarging the amount of processing units as well as the number of sources. Furthermore, the minimal throughput time, which is rising with increasing complexity, is shown. It can be observed that the curves for the due-date method and the queue length estimator show almost the same results. The due-date method shows a slightly worse performance because of sequence reordering, while the pheromone method shows inferior goal achievement. The ﬁrst two curves are almost parallel to the minimal throughput time and can be ﬁtted by linear functions, which are shown in the inset of Figure 7. This means that a constant logistical goal achievement is gained during rising complexity. The pheromone method shows an inferior behavior, which is proved by the fact that the curve can be ﬁtted by a second-degree polynomial. In this scenario, the dynamic is too high and the boundary conditions change faster than the pheromones are updated. Therefore, the pheromone method is not able to adapt to changing conditions and this effect seems to cause more problems the more complex the scenario gets. With rising complexity of the model the pheromone shows declining performance, which is caused by the fact that the pheromone method is not able to use the higher amount of degrees of freedom during frequently changing boundary conditions. In a second simulation, external dynamics is varied to determine the system’s robustness, i.e., the system’s ability to cope with external dynamics without being 30

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Figure 8: Logistical goal achievement for different mean arrival rates and multiple autonomous control methods. unstable. In this simulation, the system is called unstable if one of the system’s parameters increases without restraint. To determine this boundary of stability, the mean arrival rate at all sources has been increased and the highest possible arrival rate before the system starts to be unstable is measured. Figure 8 shows the results. The queue length estimator shows the highest robustness. The model shows stable behavior until a mean arrival rate of 0.43 parts per hour is reached. The other two methods show unstable behavior at much lower workload. They begin to destabilize at 0.35 respectively 0.36 parts per hour. This is caused by reordering in case of the due-date method and the above-mentioned inertia of the pheromone method respectively. The arrows in Figure 8 highlight the interesting measurement points in this simulation study from the dynamics perspective. The system shows altering phases of worse and improved behavior although the external dynamic is continuously enlarged. This is caused by the fact that the system shows different characteristics of internal dynamics at the different parameter constellations, which cause different performance rates. This strong interrelation between certain parameter constellations, dynamics, and performance is typical of complex systems and especially those with elements of autonomous cooperation. Those systems tend to show chaotic-like dynamics including extreme events, and their behavior strongly depends on initial conditions. It has been shown that different autonomous cooperation methods, i.e., different levels of autonomous cooperation cause different robustness and internal dynamics which result in different performances. This has been shown for different levels of complexity and external dynamics. The key ﬁnding of the simulation study is that

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the question of ﬁnding the adequate autonomous cooperation method in terms of performance improvement is context sensitive, i.e., it depends on the boundary conditions. Nevertheless, it has been shown that autonomous cooperation could be an opportunity to cope with rising complexity and dynamics but it has to be taken into account that autonomous cooperation could lead to unforeseen dynamics. In this case, it depends on the external dynamics which method to choose to reach the desired behavior of the overall system and the system is strongly sensitive to changing boundary conditions.

5 Conclusions and Future Research Requirements The question this article is aiming to answer is whether the concept of autonomous cooperation can be seen as a reasonable way to implement autopoietic characteristics into logistics systems and therewith to increase their ability to deal with complexity and dynamics. For the management of organizations and information systems (e.g., in logistical contexts), the analysis in this article was able to show that the quantity of information which organizations and their information systems have to process increases. This is due to the tendency from linear supply chains to ISN, which has been conﬁrmed during this analysis (Surana et al., 2005, Hu¨lsmann et al., 2007; Mason, 2007). Therefore, it has been outlined that the environment of actors within logistics processes is characterized by increasing complexity and dynamics (Hu¨lsmann & Berry, 2004). This leads to one of its inherent problem sources: Due to a larger number of involved actors and to a larger number of relationships between these actors in ISN, it can be stated that they are more accident sensitive than linear supply chains. In consequence, new challenges, concerning the ability of systems to cope with increasing complexity and dynamics and therefore to handle an increasing quantity of information, arise (Hu¨lsmann et al., 2007). Furthermore, it has been shown that the concept of autonomous cooperation could be an appropriate instrument to implement autopoietic characteristics like selfcreation into logistics systems. This contributes to its robustness on each of its levels, for example, decision system, execution system, as well as the information system (Hu¨lsmann & Grapp, 2006). It has been shown that ISN can be regarded as CAS as well and therefore the possibility arises to use CALS and its underlying theories as a framework to analyze their structures as well as their inherent complexity and dynamics. CALS’ inherent self-organizing characteristic leads to apparent similarities to the autopoiesis concept (e.g., Holland, 1995; Choi et al., 2001; Surana et al., 2005; Maturana & Varela, 1980; Maturana, 1999). Therewith, autopoietic systems have potentials for implementing autonomously cooperating processes and therefore increase the information-processing capacity (Windt & Hu¨lsmann, 2007). Hence, regarding an ISN as a system with autopoietic behavior can contribute to enable the organizations within an ISN and the information systems within the organizations to handle the increasing quantity of information (Hu¨lsmann et al., 2007) caused by

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increasing complexity and dynamics and therefore to keep its ability to make rational decisions (Hu¨lsmann & Wycisk, 2005). The simulation model conﬁrmed the theoretical assumption that autonomous cooperation is a possible instrument to enable an organization, an information system, or CALS as autopoietic systems to cope with the discussed challenges. By examining different methods of autonomous cooperation, it has been pointed out that different systems require different methods and different degrees of autonomous cooperation to get the best resulting system performance. Therefore, in the context of information systems, it has to be evaluated which autonomous cooperation methods and which degree of autonomous cooperation have to be chosen to generate the desired effects on the system’s ability to handle the increasing quantity of information and therefore the highest robustness and, in summary, the best performance. Further research requirements result, on the one hand, from the question which degree of the single constitutive attributes of autonomous cooperation (e.g., autonomy, interaction, etc.), on which level, and which combination between the degrees and the levels should be aspired, especially in autopoietic systems (e.g., CALS). On the other hand, it is still unclear which impacts the single degrees and the single attributes have on each other. Beside this, problems in measuring the degree of the single attributes of autonomous cooperation can be observed that have not been under closing research up to now. Furthermore, the transfer of the autonomous cooperation idea as well as the ﬁndings from research in autopoietic systems has not been completely transferred into a practical logistics context yet (Hu¨lsmann et al., 2008).

Acknowledgment This research was supported by the German Research Foundation (DFG) as part of the Collaborative Research Centre 637 ‘‘Autonomous Cooperating Logistic Processes — A Paradigm Shift and its Limitations.’’

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Chapter 16

The Autopoiesis of Decisions in School Organizations: Conditions and Consequences Raf Vanderstraeten

1 Introduction At present, education often takes place in an organized setting. From the end of the 18th century onwards, the educational system has unmistakably become differentiated — into the nonorganized family and the organized school or university. This evolution is connected with the growing complexity of modern society and with evolutions in other social subsystems, such as politics and the economy. The family context normally creates numerous moments of casual education, but it can hardly provide adequate support for lengthy and complex processes of learning. Formal organizations are able to specify and preserve the criteria necessary to steer these complex processes in the right direction. Accordingly, the introduction of compulsory schooling — in Western Europe during the long 19th century, reaching from Prussia (1764) to Belgium (1914) — has strengthened the role of organized education. How has this fact, viz. that education now takes place in an organized setting, inﬂuenced the nature of educational interaction? I want to tackle this complex question with the help of a systems-theoretical framework, inspired by the German sociologist Niklas Luhmann. Throughout his whole academic career, Luhmann (1927–1998) has paid particular attention to the systemic characteristics of organizations. In his voluminous Organisation und Entscheidung [Organization and Decision], which was published posthumously in 2000, the basic concepts of his approach are comprehensively presented. This publication provides us with a detailed overview of Luhmann’s theory of organized

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social systems. The speciﬁcation of these abstract concepts — e.g., with regard to the study of ﬁrms, hospitals, schools, or universities — remains, however, largely unknown territory (see Baecker, 1999; Seidl, 2005). Departing from Luhmann’s writings on organizational theory, as well as from some of his shorter articles on education, this chapter focuses on the analysis of educational interaction in organized social systems. Systems-theoretical analyses of social systems continue to be criticized for being uncritical, for accepting the social status quo (instead of questioning its rationale). Critics often refer to the notions of ‘‘equilibrium,’’ ‘‘homeostasis,’’ or ‘‘pattern maintenance.’’ The application of the concept of ‘‘autopoiesis’’ or ‘‘self-reproduction’’ to the study of human behavior has not stopped, but even fuelled this line of critique (e.g., Habermas, 1998; Blu¨hdorn, 2000). In contrast to this common point of view, the following analysis intends to illustrate the critical potential of systems theory. I will suggest that the analysis of the autopoiesis of educational organizations sheds light on the hidden mechanisms of this social system. To underpin my argument, I will ﬁrst highlight recent developments in the ﬁeld of systems theory and indicate their relevance for organization theory. In the second part of the chapter, education in school organizations will be analyzed from this systems-theoretical point of view.

2 Organized Social Systems In the period after the Second World War, systems theory has seen a series of ‘‘scientiﬁc revolutions’’ or fundamental reorientations of its research perspective. Its research ﬁndings have thoroughly changed the concept of system itself. Contemporary systems theory is founded upon the distinction between system and environment. How is this systems/environment theory adaptable to the analysis of social systems? And more particularly: Can organizations be analyzed as social systems of a special kind? Is it possible to reformulate the theory of organized social systems via the current state of the art in general systems theory?

2.1

Closure and Openness

The ﬁrst systems-theoretical analyses of organizations, and of the interaction between organizations and their environment, appeared after the Second World War (see Stern & Barley, 1996). They made use of the theory of open systems. The interest in this theory emerged in reaction to discussions about entropy and the so-called second law of thermodynamics. Following this thermodynamic law, closed systems inevitably evolve toward maximum entropy. For example, heat differences fade away in a particular space; warm particles cool down and cold ones heat up. Apparently only open systems are able to change the direction of this tendency, or just withstand the tendency, and to create order (negentropy) in interaction with their environment and

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thus maintain a homeostatic equilibrium.1 Inﬂuenced by the ambitions of general systems theory — viz. to level out disciplinary boundaries —, this point of view also found its way to the social sciences. In the 1960s and 1970s, systems-theoretical perspectives clearly affected organizational theory. In an inﬂuential publication of that time, Katz and Kahn wrote: ‘‘Organizations as a special class of open systems have properties of their own, but they share other properties in common with all open systems. These include the importation of energy from the environment, the throughput or transformation of the imported energy into some product form which is characteristic of the system, the exporting of that product into the environment, and the reenergizing of the system from sources in the environment’’ (1966, p. 28; 1978, p. 33). In line with these developments, organization studies shifted their focus from the internal characteristics of organizations (such as bureaucratic forms of authority, or informal organizational networks) to the problems and possibilities arising as a result of the interaction between open systems and their environment. Inspired by systems theory — although not always explicitly under its banner — there have appeared multiple inﬂuential studies on the forms of cohabitation between organizations and their environment, on the optimal characteristics of organizations active in a turbulent environment, on the gathering and processing of relevant information about the environment, on the management of risk, on dealing with uncertainty, etc. (e.g., Burns & Stalker, 1961; Lawrence & Lorsch, 1967; Thompson, 1967; Emery & Trist, 1972; Aldrich, 1979). The starting point of these analyses is the idea that an open system interacts with its environment. By way of input and output, it exchanges ‘‘material’’ with its environment. This openness provides the system with new opportunities (negentropy), but it also causes a number of problems. From this theoretical perspective, it is stressed that systems need to adapt to their chaotic and unpredictable environment if they want to maintain themselves. This way, systems-theoretical research has led to the paradoxical conclusion that an open system has to change in order to maintain its equilibrium (Buckley, 1967, 1998). But this perspective leaves a number of questions unanswered. In a new edition of The Social Psychology of Organizations, Katz and Kahn wrote: ‘‘The emphasis on openness is qualiﬁed, however y The organization lives only by being open to inputs, but selectively; its continuing existence requires both the property of openness and of selectivity’’ (1978, p. 31). How does a system establish its own distinctiveness? How does it organize itself vis-a`-vis its environment and limit its own openness? What happens when it reuses its own output as input?

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For example, Ludwig von Bertalanffy, the leading author in the ﬁeld of general system theory, wrote: ‘‘The change of entropy in closed systems is always positive, order is continually destroyed. In open systems, however, we have not only production of entropy due to irreversible processes, but also import of entropy which may well be negative. This is the case in the living organism which imports complex molecules high in free energy. Thus, living systems, maintaining themselves in a steady state, can avoid the increase of entropy, and may even develop towards states of increased order and organization’’ (von Bertalanffy, [1955]1988, p. 41).

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In this discussion, a major breakthrough came with the publication of a study on the nature of life by the Chilean biologists Humberto Maturana and Francisco Varela, entitled Autopoiesis and Cognition (1980). Its basic idea is that an organism produces, and is produced by, nothing other than itself. Simply said, a cell’s organization causes certain products to be produced (e.g., mitochondria or the nucleus). These products in turn produce the organization characteristic of that living system. The cell is organized in such a way that its processes produce the very products (units) necessary for the continuance of these processes. This means, as Maturana and Varela write, that organisms produce the products out of which they exist by the products out of which they exist. Their concept of autopoiesis (i.e., selfproduction) highlights these circular dynamics. Autopoietic systems are deﬁned as ‘‘networks of productions of components that (1) recursively, through their interactions, generate and realize the network that produces them, and (2) constitute, in the space in which they exist, the boundaries of this network as components that participate in the realization of the network’’ (Maturana, 1981, p. 21). A living organism is a system whose components and processes jointly produce these self-same components and processes, thus establishing an autonomous, self-producing entity. If one takes note of these recent developments within systems theory, which give way to another interpretation of the distinction between system and environment, it becomes possible to update the armamentarium of research on organized social systems. To put it more precisely: the concept of autopoiesis emphasizes the autonomy of living organisms. Living organisms do not import ‘‘life’’ from their environment, but need to produce their own ‘‘life.’’ Autopoietic systems are operationally closed systems (Varela, 1979; Mingers, 1995).2 The extension of this systems-theoretical armamentarium to other ﬁelds and other types of systems has been taken into consideration since the 1980s. Thoughtful pleas for the application of concepts of this kind across disciplinary boundaries have been made by several authors. In this endeavor, Luhmann’s writings play a very important role.

2.2

System and Environment

As mentioned, the German sociologist Niklas Luhmann has thoroughly explored the possibilities of an up-to-date systems theory of organizations. As part of his 2

The meaning of this term can be illustrated as follows for the domain of living organisms: ‘‘The cell y is a complex production system, producing and synthesizing macromolecules of proteins, lipids, and enzymes, among others; it consists of about 105 macromolecules on the average. The entire macromolecular population of a given cell is renewed about 104 times during its lifetime. Throughout this staggering turnover of matter, the cell maintains its distinctiveness, cohesiveness, and relative autonomy. It produces myriads of components, yet it does not produce only something else — it produces itself. A cell maintains its identity and distinctiveness during its lifespan. The maintenance of unity and wholeness, while the components themselves are being continuously or periodically disassembled and rebuilt, created and decimated, produced and consumed, is called ‘autopoiesis’’’ (Zeleny, 1981, pp. 4–5). For a discussion of the relevance of this concept in theories of socialization and purposeful education, see Vanderstraeten (2000).

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ambitious attempt to draft a new ‘‘grand theory’’ for the social sciences, Luhmann has characterized organizations (just as social systems in general) as autonomous systems which produce their own operations and which distinguish themselves in this process of self-production from their environment. Following Luhmann, organizations construct themselves by means of decisions. The speciﬁc meaning of these decisions depends upon the decision context of the organizations themselves. New decisions are connected with or built upon previous decisions. Organizations continue, specify, correct or forget their own history. ‘‘Modern organizations tend more and more to justify themselves by their own decision-making histories, in which the values of the surrounding societal system are no longer treated as obvious y Each step in the deﬁnition offers data for the next, thus constituting irreversible history where every change follows because its only rationality lies in its relation to the present state’’ (Luhmann, 1976, p. 102). Thus, if a selection committee nominates someone for appointment, after having evaluated the different candidates for the position, it is this decision which will structure the further course of decision-making. Afterwards, this person can be appointed or not be appointed — but it has now become a decision for or against this particular candidate. The organization cannot appoint another candidate without declaring itself against the nominated candidate. The initial decision sets the stage, although it does not determine subsequent decisionmaking within the organization. Organizations have to consult their own history of decision-making; they have to observe themselves. It is in this sense that they operate in a self-referential way. They individualize themselves by means of their network of decisions, by means of their form of self-organization (cf. Luhmann, 2000).3 Organized social systems become differentiated from their environment. They are caught in a ‘‘structural drift’’ when new decisions orient themselves to previous ones. This way, they incessantly create and recreate the difference between themselves and their environment. In every organization, decisions refer to previous decisions and enable new ones. Organizations (re)produce themselves. Organizations are, in this particular sense, autopoietic systems. This implies that the environment is merely able to ‘‘irritate’’ or ‘‘disturb’’ the system’s internal mode of decision-making. Luhmann writes: ‘‘In the context of autopoietic reproduction, the environment functions as irritation, as disturbance, as noise, and it only becomes meaningful for the system, when it can be related to the networks of decisions of the system’’ (1992a, p. 173). This point of view is based on classical deﬁnitions of information (when interpreted carefully). After the Second World War, in the context of general systems theory and cybernetics, Claude Shannon already indicated that ‘‘information’’ is not an inherent

3

Here, the concept of organization is used in its common (sociological) sense. This usage is different from that of Humberto Maturana. Maturana distinguishes between the structure and the organization of composite units (e.g., living organisms). Organization refers to lasting relationships in a composite unity; structure, by contrast, is the particular instantiation that a composite unity enacts at a particular moment. For example, when a human individual is born, she has one kind of structure; when she enters puberty, she has another; if she contracts a disease, she has still another. But throughout her lifetime, her organization remains the same: that which is characteristic of a living human. With regard to the study of dynamic social systems, this distinction is in my opinion not very helpful (see also Mingers, 1995).

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quality of a particular message or a fact which is communicated as such (Shannon & Weaver, 1949). The information value of a message depends upon the number of alternatives out of which the message selects. The greater the number of alternatives, the greater the information value, and the greater the reduction of uncertainty (entropy).4 In connection with these considerations, Gregory Bateson afterwards concisely deﬁned information as ‘‘a difference which makes a difference’’ (1972, p. 453). In this classical deﬁnition, Shannon’s idea of selectivity is coupled with the idea that a twofold context comes into play: a difference in the environment which makes a difference in the system. Thus, information is a product of the system itself and not an external fact which exists independent of the system’s processing of information. The information value of a stimulus always depends on the mode of selectivity of the system. A system only reacts to differences which it can distinguish itself. Its environment is only able to irritate or to disturb (see Vanderstraeten, 2001b, 2005). Classical systems theory is founded upon the distinction between open and closed systems. Its focus is on the interaction between open systems and their environment. But how is the openness of systems organized? The answer, which is provided by contemporary systems theory, is that this openness is only possible on the basis of its closure. Drawing on Maturana’s writings, Winograd and Flores, for example, write: ‘‘The structure of the organism at any moment determines a domain of perturbations — a space of possible effects the medium [i.e. the environment] could have on the sequence of structures that it could follow. The medium selects among these patterns, but does not generate the set of possibilities’’ (1986, p. 43). The environment can only be ‘‘understood’’ or constructed by means of the elements which the system produces itself. It is the internal process of decision-making in organizations that allows for environmental irritation. Thus, contemporary systems theory does not depart from the maintenance of a system’s equilibrium, but from the question how organizations distinguish themselves from their environment and reproduce this difference. It draws attention to the autonomy of systems-in-theirenvironment (see Baecker, 2001).

3 Organizing Education In the preceding sections, it has been argued that organizations are social systems of a particular kind. Organized social systems consist of decisions, and they reproduce 4

An uncomplicated example from W. R. Ashby’s An Introduction to Cybernetics might clarify this point: ‘‘Two soldiers are taken prisoner by two enemy countries A and B, one by each, and their two wives later each receive the brief message ‘I am well.’ It is known, however, that country A allows the prisoner a choice from ‘I am well - I am slightly ill - I am seriously ill,’ while country B allows only the message ‘I am well,’ meaning ‘I am alive.’ The two wives will certainly be aware that though each has received the same phrase, the informations that they have received are by no means identical’’ (1964, p. 124). Following this perspective, there is an immediate relation between information and selection. Thus, information can be related to uncertainty and entropy. Information creates order; the processing of information counteracts the natural evolution towards maximum entropy (Vanderstraeten, 2001b).

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these decisions in a network of such decisions. How does this type of systemic order inﬂuence the activities which take place in these systems? And more particularly: How does organization affect education? What are the characteristics of the autopoiesis of educational organizations?

3.1

The Autopoietic Organization

As is well-known, education relies heavily on face-to-face interaction. Education takes place in family households or in classrooms, where the physical presence of parent and child, teacher and student is guaranteed. While societal subsystems such as politics, the economy, law, or science have become less dependent on interaction situations and on the existence of personal bonds between the partners, education has evolved into another direction. This exceptional evolution is related to the fact that educational interventions aim to alter or ameliorate the student’s inner world, and that the results of this effort can best be recorded in the course of face-to-face interaction. To enable the success of education — and of other forms of ‘‘people processing’’ (e.g., therapy, conversion) — personal contact is vital (see Stichweh, 1997). Nowadays, educational interaction often takes the form of organized interaction (Vanderstraeten, 2001a, pp. 271–275). At school, students are prepared for entirely different situations; they learn things which might be of use in another context and at another moment in time (e.g., in professional life). Decisions about what is to be learned and how there is to be learned are made without consulting the family of the students. There is, however, no immediate access to the results of educational interventions. Nobody can look in the heads or souls of other human beings. A teacher can only record the patterns of external, visible behavior of her students. She has to deduce the results of her own action from these external characteristics. What can be arranged in the interaction to resolve this problem? What kind of ersatz is available if immediate observation is not possible? With regard to these questions, Luhmann (1990, 1992b) has argued that educational initiatives automatically produce a situation within which particular patterns of behavior are acceptable, while others are not. What occurs is compared with what is expected. Seen this way, the educational intention produces its own characteristic distinction. The difference between acceptable and unacceptable patterns of behavior, between approval and disapproval, between good and bad, right and wrong, etc., develops within the educational system. Particularly at school, there are numerous situations which call for selective evaluations. Here, students are continually confronted with questions, remarks, tests, exams, and other kinds of communicated expectations (Woods, 1990; Filer & Pollard, 2000; Arminen, 2005). This form of selection can be speciﬁed in a number of ways. For example, teachers can observe that one part of the students lives up to the norm and that the other part does not, or that one student is more diligent in a particular course with a particular teacher than during another course with another teacher. Students can also observe each other and assess particular differences. Moreover, students can anticipate the

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evaluations. As a consequence, there emerges a situation within which students have to reckon with new alternatives for action, and within which the consequences of their behavior are multiplied. In his famous Life in Classrooms, Philip Jackson makes similar comments: ‘‘In fact, he has three jobs. The ﬁrst, and most obvious, is to behave in such a way as to enhance the likelihood of praise and reduce the likelihood of punishment y A second job y consists of trying to publicize positive evaluations and conceal negative ones y A third job y consists of trying to win the approval of two audiences at the same time. The problem, for some, is how to become a good student while remaining a good guy, how to be at the head of the class while still being in the center of the group’’ (1990, p. 26). The point which I would like to stress is that classroom education creates these conditions itself. Educational intentions elicit a form of selection which would not emerge without these intentions. The distinctions that are introduced (such as good/wrong, positive/negative, praise/punishment, succeed/fail) are internal constructions. Educational decisions are taken in the educational setting itself (see Maclure & Walker, 2000). The meaning of evaluations is deﬁned within the educational system itself, following an internal scale. For example, satisfactory is better than unsatisfactory but less than excellent. A report mark indicates how much one can/could do better or worse. The autonomy of educational organizations depends upon this self-referential closure. To be sure, it does not depend upon true independency vis-a`-vis the environment. Its autonomy does not exclude that school organizations import knowledge from their environment, as well as the differences which are of importance in this context. Thus, the distinction between sine and cosine is not invented within education itself. But education determines who has to be able to use this distinction, and when, and what difference it makes when one does or does not know the distinction at that particular moment. Education does not distinguish between sine and cosine, but between those who are able to use the distinction and those who are not. It is only the latter distinction which determines the course of further decisionmaking in the educational system. Only with regard to this distinction, there can be no input or output (Luhmann, 1990). In other words, the distinctions which are meaningful in a system are attributable to this system.

3.2

The Socializing Organization

The order which develops within schools is an ersatz order, an organizational order. Strict control of the effects of educational interventions on the ‘‘inner world’’ of students is impossible (see Kupferberg, 1996). Instead, the system orients itself to decisions which are made within the system. At school, it becomes important to be a ‘‘good’’ student (when questions have to be answered, proﬁciency tests have to be made, or when something is undertaken on one’s own initiative). Presently, it should not come as a surprise that this way of working generates particular side effects. Unintended consequences already appear when the intention to educate comes to play itself a role in the educational interaction. Thus, the person who is educated may perceive the intention of her educator and hence gain the freedom to thwart this

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intention. She creates the opportunity to react to the intention as such and to look for other possibilities. For example, she may behave seemingly obedient, try to do it her own way, react to marginal events, or become a dedicated rebel (without noticing how much she is after all a dedicated follower of fashion). The occurrence of this kind of reactions cannot be eliminated. In view of the complexity of the situation, teachers will hardly be able to get a ﬁrm grip on what takes place in the heads of their students. It has been argued that these side effects can overtake the effects of the intentional activities and profoundly impress the students. They might be more inﬂuential than the carefully planned activities of teachers (cf., Vanderstraeten, 2000). It is not just the pedagogical intention which may provoke positive or negative reactions among students. To the contrary, the particularities of organized educational interaction also mark students. Organized interaction in classrooms is characterized by extreme expectations (in comparison with the expectations in other spheres of social behavior). Think of the asymmetrical relation between the teacher and her students, the enforced discipline, the imperative to sit still and preserve silence, the limited tolerance for diverging opinions, the tyranny of schedules and curricula. Students are mostly only able to face the teacher and not each other. The classroom is isolated from the world; this world only enters in the preﬁgured form of ‘‘learning material.’’ A period is ended when the bell has rung and not when the students’ attention slackens or when the teacher’s efforts have been successful (see Hammersley, 1990, pp. 101–113; Dreeben, 2000). Hitherto, the effects of these atypical structures have most of all been discussed in terms of the ‘‘hidden curriculum.’’ Mainstream research has emphasized the reproduction of power relationships in schools — its consequence being that progressive teachers have become demoralized (see Burbules, 1993, pp. 131–142). From a contemporary systems-theoretical point of view, however, it seems useful to reframe the question about the socialization effects of organizations. Departing from the autonomy of organizations, one gains a more solid basis for research about the effects of decisionmaking structures. One might, for example, consider the hypothesis that particular organizational structures (such as the schemes of good/wrong or succeed/fail, as well as arrangements about entrance regulation or graduation) impose themselves in such striking ways that they dominate the entire socialization process in schools and bread off other decision-making structures. Unintended side effects are not uniquely characteristic of educational organizations. But schools appear to be particularly vulnerable. Even in comparison with other ‘‘people processing’’ organizations, they ﬁnd themselves in a difﬁcult position. In the ‘‘people processing’’ organizations, in which professional help is offered, clients often participate as a consequence of biographic crises and do not need to be urged on to cooperate. They are longing for help and often ready to pay a lot of money for it (Vanderstraeten, 1999). Because children have to go to school (at least until the compulsory school age), this source of commitment and dedication mostly fails. In fact, one ﬁnds numerous forms of opting-out behavior in educational institutions. Students constantly observe the activities of their teacher. Observing whether one is being observed, or is temporarily out of the teacher’s sight, pretending that one listens attentively, hiding behind the back of a classmate, looking as if all the subject material

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is perfectly understood, etc. In short, the decision-making structures of schools produce themselves an entire set of opting-out behavior. From a pedagogical point of view, these behavioral patterns can certainly not be described as desirable states (Kalthoff & Kelle, 2000). The unintended and mostly also unforeseen effects of classroom education cannot be eliminated by way of a careful selection of subject material — not even if this selection is informed by the professional perspectives of the students, or by their current lifeworld and interests (Margolis, 2001). They inevitably turn up in educational contexts. The few remarks which Niklas Luhmann has made on the issues of socialization and education in his major works Social Systems ([1984]1995) and Die Gesellschaft der Gesellschaft (1997) exactly deal with these uncontrollable side effects. They form the background of his critical and pessimistic attitude vis-a`-vis the role of the educational system in modern society. Luhmann writes: ‘‘The autonomy of a differentiated input/output arrangement must then submit to correction a reality it has itself created and direct its counterintuitive behavior back to reality. A system that is structured too improbably and that tries to identify itself entirely with the transformation of input into output ends up having to deal with the problems resulting from its own increase-directed reductions’’ (1995, p. 207; cf., 1997, pp. 976–978). Educational organizations do not function in a rational manner; their presupposed rationality and reliability lead to problems which they cannot control themselves. One cautionary note is in place, however. The preceding analyses do not support pleas for a thorough reorganization of schools, nor for ‘‘deschooling society’’ (Illich, 1983). A reorganization will not eliminate the basic characteristics of educational organizations (see Farrell, 2000). The preceding analyses rather attempt to gain insight in the characteristics of modern society and its school organizations by reorienting the assumptions of organization studies and by highlighting the special role of organizations in modern society.

4 Conclusion The foregoing discussion has provided a global overview of the effects of the organized school setting on educational interaction. To conclude this paper, some further remarks can be made which might direct future research into new directions. (a) It has been convincingly demonstrated that the relation between individuals and society currently takes the form of a lifelong career (e.g., Beck, 1986). Integration within society is less dependent upon an individual’s natural characteristics, as it is upon particular accomplishments in the course of her career. The modern world is a world of self-management. Moreover, career planning highly relies on educational organizations such as schools and universities (Kurtz, 2001, 2005). Education contributes to career development — not that much via activities which change the ‘‘inner world’’ of students (but cannot prove this inﬂuence), but via its selection mechanisms and the degrees and certiﬁcates which it grants. What

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does or does not happen at the beginning of one’s career can have far-reaching results. What has been achieved, mostly effects what becomes possible. Expectations are formulated on the basis of the trajectory which has been traveled. In the educational system, there are not many alternatives available. School careers appear as an almost automatic succession of sequences (as long as everything is ‘‘normal’’). Students pass from course to course, from year to year, from degree to degree. The standardization of school careers does not only facilitate but also provoke comparisons. It generates a high pressure to perform in accordance with the organization’s criteria. School success has become an important item for students and their parents. Its impact on individual careers might explain the rapid educational expansion of the last decades of the 20th century (see Collins, 1979; Vanderstraeten, 1999, 2000). (b) Educational interventions automatically elicit different forms of selection. Attempts at ‘‘people processing’’ and educating inevitably produce the distinction between good and wrong, between conform and deviant behavior. Educational organizations extend and amplify these forms of selection. It is this situation which seems to inspire the numerous plans and projects for innovation which constantly call into question the habits established within the educational world. Apparently, the educational establishment experiences this selection as a Fremdko¨rper, as a function that society imposes upon school organizations and that goes to the detriment of authentic education. Educators incessantly stress the fundamental equality of all people; they reject selection mechanism which only focus on performance criteria and distinguish students on the basis of ‘‘superﬁcial’’ characteristics such as test results. In other words, educational ideals continue to clash with the logic of school education. But why does the ﬁeld of education identify itself with reform, with ‘‘reforming again, again, and again’’ (Cuban, 1990)? For a reorganization cannot put the organizational logic of schools out of action. That is one lesson to be learnt from the failures of reform efforts in school education. Therefore, one might presuppose that the pedagogical rhetoric serves some latent functions, such as the legitimization of the educational establishment itself. (c) From the end of the 18th century, an educational establishment has emerged in several countries in Europe. It started, for example, with the construction of several ‘‘showcase’’ schools — such as those founded at the end of the Enlightenment by so-called philanthropists. In the same context, it was also argued that teachers themselves could not be carriers of a careful reﬂection on the conditions of education, and certainly not of a systematic reform movement. This establishment has expanded in the 19th and 20th century. It has left its mark on the evolution of the educational system. This group not only concentrates on the tasks and problems of teachers, but also develops its own dynamics. It has an interest in legitimizing its own existence. Instructional problems may not disappear because they get solved. Moreover, the activities of this group increase the contingency with which teachers are confronted. Whatever, for example, the phrase ‘‘key qualiﬁcations’’ may express and whatever it may encompass: what it includes is declared as something that can be decided. ‘‘Decision’’ also means

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Raf Vanderstraeten being permanently confronted with ‘‘revision.’’ Reform, innovation, renewal, or progressivism all become valued concepts for the establishment — whereas in comparison, teachers (and other audiences, such as parents and students) are depicted as conservative elements, whose resistance then explains the failure of the reforms. The activity of this group thus also affects the teachers’ claims of professional authority (Bourdieu, 1986). The history of education probably cannot be written without giving due attention to the processes of interaction and competition between these different groups in the organization (Vanderstraeten, 2007).

These remarks further illustrate the critical potential of systems theory. They indicate its potential for exploring the basic mechanisms which structure the ﬁeld of organized education. Seen against this background, the widespread reproaches about the conservatism of social systems theory are totally undeserved. But this does not mean that systems theory can promise and produce a better world. The preceding analyses do not provide a straightforward path to reform proposals, which stress the value of particular ideals. Critique is useful, when the expectations from which it departs are themselves controlled by research outcomes and when its ideals can be realized in our social world. The analyses in this chapter have departed from Luhmann’s idea that social systems can be described as speciﬁc types of autopoietic systems. Like in the case of living systems, autopoiesis or self-referential closure here does not mean that a social system is not be affected by its environment. But it does mean that a social system can react to its environment only in accordance with its own mode of operation. A social system, such as an organization, is closed with respect to the meaningful content of communicative acts; meaning here can be actualized only by circulation in the network of ongoing decisions. In comparison with open-systems theory, this autopoietic view of social systems provokes remarkable shifts — for example, from an interest in planning and control to an interest in autonomy and environmental sensitivity, and from structural stability to dynamic stability. As indicated in my preceding remarks and examples, these shifts make it necessary to analyze the mechanisms that are used to establish and maintain boundaries between system and environment.

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