Competitive Strategy
Competitive Strategy Options and Games
Benoît Chevalier-Roignant Lenos Trigeorgis
The MIT Press Cambridge, Massachusetts London, England
© 2011 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. For information about special quantity discounts, please email
[email protected] .edu This book was set in Times Roman by Toppan Best-set Premedia Limited. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Chevalier-Roignant, Benoit, 1983– Competitive strategy: options and games/Benoit Chevalier-Roignant and Lenos Trigeorgis; foreword by Avinash K. Dixit. p. cm. Includes bibliographical references and index. ISBN 978-0-262-01599-8 (hardcover: alk. paper) 1. Options (Finance) 2. Investment analysis. 3. Game theory. I. Trigeorgis, Lenos. II. Title. HG6024.A3C48535 2011 332.6—dc22 2010053619 10
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Contents
Glossary xi Symbols xix Foreword by Avinash Dixit Preface xxv 1
The Strategy Challenge
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1
1.1 1.2 1.3
The Changing Corporate Environment 3 What Is Strategy? 10 Two Complementary Perspectives on Strategy 1.3.1 Corporate Finance and Strategy 15 1.3.2 Game Theory and Strategy 20 1.4 An Integrative Approach to Strategy 35 1.5 Overview and Organization of the Book 41 Conclusion 43 Selected References 43 I
STRATEGY, GAMES, AND OPTIONS
2
Strategic Management and Competitive Advantage 2.1
2.2
15
45 47
Strategic Management Paradigms 48 2.1.1 External View of the Firm 48 2.1.2 Internal View of the Firm 50 Industry and Competitive Analysis 50 2.2.1 Macroeconomic Analysis 57 2.2.2 Industry Analysis: Structure–Conduct–Performance Paradigm 58 2.2.3 Porter’s Industry and Competitive (Five-Forces) Analysis 59
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Contents
2.3
Creating and Sustaining Competitive Advantage 2.3.1 Value Creation 66 2.3.2 Generic Competitive Strategies 70 2.3.3 Sustaining Competitive Advantage 72 Conclusion 73 Selected References 74 3
Market Structure Games: Static Approaches
66
75
3.1 3.2
Monopoly 76 Duopoly 81 3.2.1 Bertrand Price Competition 86 3.2.2 Cournot Quantity Competition 92 3.2.3 Strategic Substitutes versus Complements 97 3.3 Oligopoly and Perfect Quantity Competition 99 3.4 Market Structure under Incomplete Information 102 Conclusion 107 Selected References 107 4
Market Structure Games: Dynamic Approaches
109
4.1
Commitment Strategy 110 4.1.1 Concept of Commitment 111 4.1.2 Taxonomy of Commitment Strategies 118 4.1.3 Sequential Stackelberg Game 131 4.2 Bargaining and Cooperation 135 4.2.1 Bargaining 135 4.2.2 Cooperation between Cournot Duopolists in Repeated Games 138 4.2.3 Co-opetition: Sometimes Compete and Sometimes Cooperate? 147 Conclusion 151 Selected References 151 5
Uncertainty, Flexibility, and Real Options 5.1
5.2
153
Strategic Investment under Uncertainty—The Electricity Sector 154 5.1.1 Need for New Investment in Europe 154 5.1.2 Sources of Uncertainty 156 5.1.3 Generation Technologies and Business Risk Exposure 159 Common Real Options 162
Contents
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5.3
Basic Option Valuation 169 5.3.1 Discrete-Time Option Valuation 176 5.3.2 Continuous-Time Options Analysis 184 Conclusion 189 Selected References 189 Appendix 5A Multistep Cox–Ross–Rubinstein (CRR) Option Pricing 190 II
OPTION GAMES: DISCRETE-TIME ANALYSIS
6
An Integrative Approach to Strategy: Option Games
193
6.1
195
Key Managerial Issues: Optimal Timing and Flexibility versus Commitment 196 6.1.1 Optimal Investment Timing under Uncertainty 196 6.1.2 The Trade-off between Flexibility and Commitment 197 6.2 An Illustration of Option Games 197 6.3 Patent-Fight Strategies 206 6.4 An Application in the Mining/Chemicals Industry 209 Conclusion 217 Selected References 217 7
Option to Invest
219
7.1 7.2
Deferral Option of a Monopolist 219 Quantity Competition under Uncertainty 224 7.2.1 Cournot Duopoly 224 7.2.2 Asymmetric Cournot Oligopoly 235 7.3 Differentiated Bertrand Price Competition 238 Conclusion 240 Selected References 242 8
Innovation Investment in Two-Stage Games 8.1 8.2
243
Innovation and Spillover Effects 243 Innovation and Patent Licensing 253 8.2.1 Patent Licensing: Deterministic Case 253 8.2.2 Patent Licensing under Uncertainty 261 8.3 Goodwill/Advertising Strategies 264 Conclusion 271 Selected References 272
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Contents
III
OPTION GAMES: CONTINUOUS-TIME MODELS
9
Monopoly: Investment and Expansion Options
275
277
9.1
Option to Invest (Defer) by a Monopolist 278 9.1.1 Deterministic Case 281 9.1.2 Stochastic Case 284 9.2 Option to Expand Capacity 298 9.2.1 Additional (Lumpy) Capacity Investment 299 9.2.2 Incremental Capacity Investment 303 Conclusion 306 Selected References 307 Appendix 9A Contingent-Claims Analysis of the Option to Invest in Monopoly 308 10
Oligopoly: Simultaneous Investment
311
10.1 Oligopoly: Additional Capacity Investment 312 10.1.1 Existing Market Model: Expansion Option 312 10.1.2 New Market Model: Investment (Defer) Option 314 10.2 Oligopoly: Incremental Capacity Investment 317 10.3 Perfect Competition and Social Optimality 322 Conclusion 325 Selected References 325 Appendix 10A Derivation Based on Dynamic Programming 326 11
Leadership and Early-Mover Advantage
331
11.1 A Basic Framework for Sequential Investment in a Duopoly 331 11.2 Duopoly with Sequential Investment under Uncertainty 339 11.3 Oligopoly with Sequential Investment under Uncertainty 348 11.4 Option to Expand Capacity 352 Conclusion 357 Selected References 357 12
Preemption versus Collaboration in a Duopoly
359
12.1 Preemption versus Cooperation 360 12.1.1 Preemption 363 12.1.2 Cooperation in an Existing Market 370 12.2 Option to Invest in a New Market under Uncertainty
372
Contents
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12.2.1 Symmetric Case 373 12.2.2 Asymmetric Case 379 12.2.3 Size of Competitive (Cost) Advantage 382 12.3 Option to Expand an Existing Market 389 12.3.1 Symmetric Case 389 12.3.2 Asymmetric Case 393 Conclusion 395 Selected References 396 Appendix 12A Strategy Space and Solution Concept 397 Appendix 12B Perfect Equilibrium in Deterministic Setting 398 Appendix 12C Perfect Equilibrium in Stochastic Setting 400 13
Extensions and Other Applications
403
13.1 Exogenous Competition and Random Entry 404 13.2 Real-Estate Development 405 13.3 R&D and Patenting Applications 407 13.4 Investment with Information Asymmetry 411 13.5 Exit Strategies 415 13.6 Optimal Capacity Utilization 417 13.7 Lumpy Capacity Expansion (Repeated) 419 13.8 Other Extensions and Applications 421 Conclusion 423 Selected References 423 Appendix: Basics of Stochastic Processes
425
A.1 Continuous-Time Stochastic Processes 426 A.1.1 Brownian Motion 427 A.1.2 Mean-Reversion Process 438 A.1.3 General Itô Processes 440 A.2 Forward Net Present Value 441 A.3 First-Hitting Time and Expected Discount Factor 447 A.3.1 Exercise Timing and First-Hitting Time 447 A.3.2 Expected Discount Factor 448 A.3.3 Profit-Flow Stream with Stochastic Termination 452 A.4 Optimal Stopping 453 Conclusion 458 Selected References 458 References 461 Index 473
Glossary
Accommodated entry Entry is accommodated if structural entry barriers are low and entry-deterring strategies are ineffective or too costly. An incumbent attempts to adopt strategies and build competitive advantage early on to cushion the future negative effects of accommodated competitive entry. Action An action or move by one of the players in a simultaneous game is a choice or decision she can make that affects the other player(s). Barriers to entry Barriers or factors that allow an incumbent firm to earn positive economic profits or excess rents by making it unprofitable for newcomers to enter the industry. Competitive markets are characterized by low entry barriers. Blockaded entry A condition where the incumbent need not undertake any entry-deterring strategies to enjoy monopoly rents in the marketplace. Structural or administrative barriers are sufficient conditions for blockaded market entry. Call option A contract or situation that gives its holder the right, but not the obligation, to buy or acquire an underlying asset at a predetermined price over a specified period (maturity). Chicken game
See “war of attrition.”
Closed-form solution A solution that gives an analytical answer to a mathematical formulation. Closed-loop strategies In dynamic games, closed-loop strategies allow players to condition their play on both the previous moves by the players and on calendar time. All past actions of all players is common knowledge at the beginning of each stage. Closed-loop equilibrium The equilibrium forms a perfect equilibrium in closed-loop strategies; that is, the closed-loop strategies form a Nash
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Glossary
equilibrium in each and every subgame (on and off the equilibrium path). Commitment
See “strategic commitment.”
Commitment value The incremental value (positive or negative) accruing to a firm from making a strategic commitment. Cost advantage One of the main strategies to achieve a competitive advantage is to seek to attain lower costs, while maintaining a perceived benefit comparable to that of competitors. A relative cost advantage influences the optimal investment timing of competing firms. Cost of capital The rate of return expected or required by an equally risky asset or investment to induce investors to provide capital to the firm. The cost of capital reflects the systematic (or market) risk of a traded asset perfectly correlated with the investment or asset to be valued. Deterred entry A situation occurring when an incumbent can keep an entrant out by employing an entry-deterring strategy. Dominant strategy A strategy that is the best decision for a player regardless of the action or strategy chosen by its opponent. Early-mover advantage An advantage from moving early that enables a firm to make a higher economic profit than its rivals. An early-mover advantage can stem from the uncontested market presence of a leader that enjoys monopoly rents for some time. Sources of sustainable earlymover advantages include the learning-curve effect (economies of cumulative production and learning), brand-name reputation—especially in situations where buyers are uncertain about product quality (“experience goods”)—, or high customer switching costs. Economic profit A profit concept that represents the difference between the profits earned by investing resources in a particular activity and the profits that would have been earned by investing the same resources in the best alternative activity in the market. Opportunity costs are subsumed in economic profits. Economies of scale Cost savings achieved when the unit production cost of a product decreases with the number of units produced. Economies of scope These involve cost savings externalities among product lines or production activities. Elasticity The elasticity of a variable with respect to a given parameter is the percentage change in the value of the variable resulting from a one
Glossary
xiii
percent change in the value of the parameter, all other factors remaining constant. European option An option that can only be exercised at maturity, not earlier. Extended (or expanded) net present value (E-NPV) The total value of a project including the option or flexibility value and the impact of any strategic commitment or interaction effects. Fixed costs Costs that are independent of the scale of production and are locked in for a given period of time. Some distinguish among fixed and sunk costs though the difference is subtle: “fixed” often refers to short-term commitments, while “sunk” or nonrecoverable costs generally involve a longer planning horizon. Focal-point argument In the event of multiple equilibria, the focalpoint argument suggests that an equilibrium that appears “natural” or logically compelling is the one more likely to arise. Games of incomplete information Games where players do not know some relevant characteristics of their opponents (e.g., their payoffs, available action sets or their beliefs). Games of perfect information Games where all players possess all relevant information. Extensive-form games of perfect information have the property that there is exactly one node or decision point in each information set. Game theory A branch of mathematics and economic sciences concerned with the analysis of optimal decision-making in multiplayer settings. Chess is a well-known example. Solution concepts meant to provide predictions on the likely outcomes impose certain behavioral restrictions on players. Information set The information a decision maker has at the moment she makes a decision. Isolating mechanisms Economic forces put in place by a firm to limit the extent to which its competitive advantage can be duplicated, eroded, or neutralized through the resource-creation activities of competitors. Learning-curve effect Learning-based cost advantage that results from accumulating experience and know-how in productive activities over time. Market structure Characteristic of a market in terms of number and size (power) distribution of firms. Monopoly, duopoly, and perfect competition are classic examples of market structure.
xiv
Glossary
Markov property This property asserts that for certain stochastic processes or dynamic problems, all the past relevant information is summarized into the latest value of a variable or price. One cannot therefore use past information to predict the future state of the variable. Efficient markets and Itô processes have this Markov property. Maturity The period or last moment at which an option can be exercised. If the option can be exercised before this date, it is called an American option; if only at maturity, a European option. Myopic strategy A strategy that does not take into account or is independent of the investment decisions of rivals. Nash equilibrium A classic equilibrium solution concept used in the analysis of multiplayer games: each player pursues its individual optimizing actions given the best actions of the other players. In equilibrium, no player has an incentive to unilaterally deviate. Nature Acts of nature are treated as the actions of a quasi-player that makes random choices at specified points in a game. Net present value (NPV) The NPV paradigm is well established in corporate finance. The net present value of a project is the present value of the expected cash flows minus the (present value of) required investment costs. In the absence of managerial flexibility, a stand-alone investment is deemed acceptable if its net present value is positive. Node A decision point in a game at which a player (or nature) can take an action. Open-loop strategies Strategies where players cannot observe the previous play of the opponents and therefore condition their play on calendar time only. Such strategies are also called “precommitment strategies.” Open-loop equilibrium A Nash equilibrium solution that is obtained when firms adopt open-loop strategies ignoring their rivals’ actions over the history of the game. Option A contract or situation that gives its holder the right but not the obligation to buy or acquire (if a call) or sell (if a put) a specified asset (e.g., common stock or project) by paying a specified cost (the exercise or strike price) on or before a specified date (the expiration or maturity date). If the option can be exercised before maturity, it is an American option; if only at maturity, a European option. Option to invest or option to defer An American-type call option embedded in projects where management has the right (but no obliga-
Glossary
xv
tion) to delay the project start for a certain time period. The exercise price is the cost needed to initiate the project. Option valuation Valuation process by which the total value or “expanded net present value” (E-NPV) of an investment opportunity is determined. The valuation approach is meant to capture management’s flexibility to adapt its decisions to the evolving uncertain circumstances. Path dependence A situation occurring when past circumstances or history condition the current outcome and can preclude or favor certain path evolutions in the future. Payoff The utility, reward, or value a player receives when the game is played out. In the option games context, the payoff can be the real option value (e.g., the value of a plant with the option to expand production). Perfect Nash equilibrium
See “subgame perfect Nash equilibrium.”
Players The individuals, firms, or actors that make decisions in a game situation. Each player’s goal is to maximize her payoff or value by choosing the best action or sequence of actions (strategy). Precommitment strategies
See “open-loop strategies.”
Preemption A situation whereby a firm invests ahead of its rivals to hinder their entry or profitable operation. Such a situation is often related to the presence of some first-mover advantage. Proprietary option An option held by a firm that entitles it to the full exclusive benefits resulting from exploiting the option. A monopolist firm has a proprietary investment option. Real option The flexibility arising when a decision maker has the opportunity to adapt or tailor a future decision to information and developments that will be revealed in the future. A real option conveys the right, but not the obligation, to take an action (e.g., defer, expand, contract, or abandon a project) at a specified cost (the exercise price) for a certain period of time, contingent on the resolution of some exogenous (e.g., demand) uncertainty. Real options analysis The field of application of option-pricing theory to valuing real investment decisions. Risk-neutral valuation A valuation method underlying option pricing analysis that adjusts for risk in the expectation of cash flows (certaintyequivalent), enabling discounting of future values at the risk-free interest rate. This contrasts to the standard valuation approach (NPV) consisting
xvi
Glossary
in discounting the (actual) expected cash flows at a (higher) risk-adjusted discount rate. Risk neutral Situations where investors are indifferent between a sure payoff (certainty-equivalent) and a risky outcome of equal expected value. By extension, the same description is used for the corresponding valuation method. Shared option An option simultaneously held or shared by several firms in the industry. Shared options characterize competitive industries where incumbent firms share the same investment opportunities. Shared options are more involved to analyze as they must account for rival reactions or interactions. Option holders’ investment behavior and project values are affected by the proprietary or shared nature of real options. Soft commitment (or accommodating stance) A strategic investment commitment that makes the rival firm better off in the (later) competition stage. In Cournot quantity competition, a soft commitment leads the committing firm to produce relatively less, while in Bertrand price competition a soft commitment induces the firm to maintain a higher price. Stochastic processes A set or collection of random variables such that the value of the process at any future time t is random though specified by a given probability distribution. Strategic commitment A strategic decision or move intended to alter the competitors’ behavior or beliefs about future market competition. Such commitments are generally difficult or costly to reverse. Strategic complements (or reciprocating actions) These characterize actions in situations where firms react (in equilibrium) in a reciprocating or complementary manner (i.e., reaction functions are upward sloping). For example, I will be nice to you if you are nice to me. In case of price competition, price-setting actions are strategic complements. Strategic substitutes (or contrarian actions) These characterize actions in situations where firms react (in equilibrium) in a contrarian or opposite manner (i.e., reaction functions are downward sloping). For example, you take advantage of me if I am nice to you. In case of quantity competition, capacity-setting actions are strategic substitutes. Strategy A behavioral rule adopted by a player that prescribes which contingent action(s) to choose at each stage in a game. A strategy specifies for each decision node or information set which action to pursue. Open-loop and closed-loop strategies refer to different strategy types:
Glossary
xvii
open-loop strategies make more restrictive assumptions on the information the players possess over the game play. Subgame perfect Nash equilibrium Solution concept used in dynamic games under complete information: (continuation) strategies form a Nash equilibrium at each and every stage of the game—even those stages that will not be actually played in equilibrium (“off the equilibrium path”). Sunk costs Investment costs that cannot be recouped. Once incurred, sunk costs are irrelevant for future decision-making. Tough commitment (or aggressive stance) A strategic commitment intended to hurt the rival. In Cournot quantity competition, tough commitment induces the committing firm to produce more, while in Bertrand price competition the firm will cut its price and enter a price war. Variable costs Costs, such as direct labor or commissions to sales people, that vary as the output level rises. War of attrition (or chicken game) In a duopoly involving a secondmover (follower) advantage, both firms have an incentive to be a follower or wait to be the last to move, leading to a “war of attrition.”
Symbols
i, j
Competing firms (in a duopoly)
−i
All other firms except firm i (in oligopoly)
n
Number of firms (in a competitive industry or market)
π i (⋅) π i (⋅)
Firm i ’s certain (deterministic) profit function
p (⋅)
Deterministic (inverse) demand function
p (⋅)
Uncertain (inverse) demand function
a, b
Known constant parameters in the (inverse) demand function
qi
Quantity produced by firm i
Q
Total quantity produced by all firms in the industry
ci
Firm i ’s variable (unit) cost
Ki
Up-front strategic investment outlay by firm i (in commitment games)
s
Degree of substitution (in differentiated Bertrand price competition)
x γ
R&D effort (in R&D investment games) Degree of R&D spillover (sharing) effects
k
Risk-adjusted discount rate (cost of capital)
r
(Instantaneous) risk-free interest rate
δ
Asset cash flow or dividend yield, convenience yield (for commodities), competitive erosion, or opportunity cost of waiting
g
Actual growth rate (drift parameter) for geometric Brownian motion
gˆ
Risk-neutral growth rate (for geometric Brownian motion)
Firm i ’s uncertain (stochastic) profit function
xx
Symbols
α αˆ
Actual growth rate for arithmetic Brownian motion
σ
Volatility (for the arithmetic or geometric Brownian motion)
h
Small time interval
dt
Infinitesimal time interval (h → dt )
q
Actual (empirically observed) probability (of up move)
p
Risk-neutral probability (of up move)
u
Up-move multiplicative factor in a binomial tree (process)
d
Down-move multiplicative factor in a binomial tree (process)
Ii
Investment outlay (cost) for firm i
e
Expansion factor (base value V expanded by e percent upon paying investment cost I , giving eV − I)
λ
Poisson jump arrival rate (frequency)
ρ
Correlation coefficient
E0 [⋅]
Expectation based on actual probabilities (conditional on time- 0 information)
Eˆ 0 [⋅]
Risk-adjusted expectation based on risk-neutral probabilities (conditional on time-0 information)
var ( )
Variance (or σ 2 )
t0
Starting time
t
Current time
T T
Time (years) to maturity of the option (in discrete-time models)
T * Bt (T )
Risk-neutral growth rate for arithmetic Brownian motion
random first-hitting time (i.e., the random time at which the pre-set investment trigger XT is first reached) Optimal random first-hitting time (i.e., the random time the optimal investment trigger X * is first reached) Value (at time t ) of a bond paying 1 euro at random future time T . It also represents the expected discount factor.
zt
A standard Brownian motion or Wiener process
εp
Price elasticity of demand (ε p (Q) ≡ −[∂Q ∂p] × ( p Q))
β (βˆ )
Dummy variable of the fundamental quadratic function (^ for the risk-neutral version)
β1 (βˆ 1) Positive root of the fundamental quadratic function (elasticity of the option to invest)
Symbols
xxi
β 2 (βˆ 2 ) Negative root of the fundamental quadratic function (elasticity of the option to exit) x0
Time-0 value of the stochastic process in the case of arithmetic Brownian motion
x t
Stochastic process value at time t in the case of arithmetic Brownian motion ( x t ≡ ln ( X t ))
X0
Time-0 value of the stochastic process in the case of geometric Brownian motion
X t
Time-t value of the process in the case of geometric Brownian motion
XT
Value of the stochastic process at maturity T in discrete-time option games, or the preset investment trigger—not necessarily the optimal one—in continuous-time option games
X*
Optimal investment trigger (*)
M
Monopolist firm’s total value (with investment timing or expansion option)
S
Total firm value (with investment timing or expansion option) in an oligopoly characterized by simultaneous investment
L
Leader’s value (with investment timing or expansion option) in sequential investment option games
F
Follower’s value (with investment or expansion option) in sequential investment option games
*
(Superscript) denotes optimal or equilibrium value
^
(Superscript) denotes risk-neutral or risk-adjusted expectation, variable or parameter Euro
M
Million
Bn
Billion
≡
Means “is defined to be”
Foreword
President Truman once said: “Give me a one-handed economist. All of my economic advisers say ‘On the one hand this, on the other hand that.’” Economists do indeed recognize that there are multiple forces at work in most situations, and it takes quite subtle analysis to understand their interaction and balance. This book is an admirable effort at such an economic analysis. When facing an uncertain future, remaining flexible until more information arrives has value, because one can cherry-pick to make investments only when the prospects are relatively favorable. This is the starting intuition of real option theory. But in game theory, the strategy of making irreversible commitments to seize first-mover advantage and present rival players with a fait accompli to which they must adapt can have its own value. So what does one do when facing an uncertain future in the company of rivals? This is a difficult problem, conceptually as well as mathematically. The last two decades have brought a trickle of research contributions that address some aspect of this dilemma. In this book, Benoît Chevalier-Roignant and Lenos Trigeorgis synthesize and consolidate much of this literature and skillfully extend it. Generations of students and researchers over the next decade or two will find the book an invaluable starting point for their own work. The authors also deserve congratulations for their excellent exposition. They begin with very simple and clear overviews of the issues, concepts and models from the separate areas of real options and game theory. They focus on applications to industrial organization and business. For students in any of these areas, this book can serve as a hypermarket for one-stop intellectual shopping. I will endeavor a personal remark. I was involved in some of the early research on real option theory. I also contributed to popularizing game theory. But I seem to suffer from a kind of attention-deficit disorder in
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Foreword
research; therefore I got interested in, and diverted to, other fields like political economy and governance institutions. I am happy that my contributions to real option theory are still remembered, and feel honored to be asked to write a preface to this book. But, returning to the real options literature after many years to read this book, I also feel like Rip Van Winkle, amazed at how much has changed in the intervening years. Although I have enjoyed my excursions into other fields, perhaps I should have stayed and contributed to these equally exciting developments that have combined options and games to produce better twohanded economists. Avinash Dixit Princeton, NJ June 2010
Preface
In real life most situations corporate managers face are characterized by both strategic and market uncertainties with respect to the economic environment. Following the liberalization and deregulation of Western economies, very few industries remain protected, whereas most companies face fierce competition in their respective economic sectors. Certain European governments used to favor high administrative barriers shielding certain “natural monopolies” from competitive entry. Such protected monopolies included the telecommunications, electricity, and gas sectors. European governments recently had to enforce deregulation schemes, opening up many economic activities to new, potentially foreign market participants. At the same time sectors traditionally populated by many firms have undergone significant consolidation, yielding oligopolistic situations with a reduced number of players. These two ongoing concurrent phenomena—liberalization and continuing consolidation— highlight the emphasis corporate managers increasingly put on strategic uncertainty and market structure. Besides strategic uncertainty, managers face increasing market uncertainty. With a reduced life cycle for many products, firms can no longer rely on a given offering but have to renew their product portfolio frequently to sustain or enhance their revenue stream in light of competitive pressures. The IT industry has evolved most rapidly, putting companies unable to respond to market developments and technological uncertainty at a severe disadvantage. At the core of this dilemma lies a classic trade-off between commitment and flexibility. Managers can stake a claim by making large capital investments today influencing their rivals’ behavior or take a “wait-andsee” or step-by-step approach to avoid possible adverse market consequences tomorrow. The assessment and optimal management of strategic options is critical for firms to succeed in today´s constantly changing
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Preface
business environment. Whether to invest in a new product or enter a new market is a critical decision management should address with proper analytical tools in assessing options and deciding whether to make this or that strategic move. This book aims to make accessible to a wide audience recent results on how to achieve and quantify balance between flexibility and commitment through the new approach of “option games.” We believe that this approach can play an important role in managing modern business in a changing global marketplace. Option games help model situations where a firm that has a real option to (dis)invest in specific projects faces competition. Such situations lead to two, sometimes conflicting, main sources of value for the firm. First, there is a value of waiting or flexibility related to the real option that the firm holds to make future better-informed decisions. Second, there is a value of commitment in light of the strategic interaction with competitors. The trade-off between these two forces calls for a careful balancing act. From a modeling perspective, the examination of such a trade-off calls for a combination of the real options and game theory approaches to decision-making. In the first part of the book, we discuss prerequisite concepts and tools concerning basic game theory, industrial organization, and real options analysis. We are then in a position to amalgamate these diverse fields into a unified framework for option games. This makes the task at hand very ambitious because these fields of research normally require different tool kits and needed results are scattered around in disparate parts of the literature. We aim to fill this gap here by synthesizing the existing literatures to provide a consistent and accessible account of options and games. We combine some of the best materials, tools, and ideas found in diverse books and research works on game theory (e.g., Fudenberg and Tirole 1991; Osborne 2004), industrial organization (Tirole 1988), and real options analysis (Dixit and Pindyck 1994; Trigeorgis 1996) and go beyond current knowledge to chart new territory. The current book brings important materials and ideas together in a unified framework, which makes it much easier for managers and academics to enter and understand this new field. The second part of the book presents the new approach in discrete time, while the last part on continuous-time option games is not covered in any systematic way anywhere and is an important addition. For pedagogical convenience and for the sake of simpler and clearer exposition and buildup of the basic ideas, we first present each of the building blocks step by step to form the supporting foundation and columns of the structure, with the richer theory coming later
Preface
xxvii
(culminating toward chapter 12) as the keystone that will complete the arch.1 The book should be especially appealing to the academic community, particularly in the areas of strategy, economics, and finance. It is the first book that combines the aforementioned fields in such a way that it is accessible to an audience that is not necessarily expert in all fields involved. The book should also be of interest to (academically trained) practicing managers who are interested in applying these ideas. It provides many strategic insights and a ready-to-use tool kit offering quantitative guidance for important competitive trade-offs faced by the modern firm. We attempted to strike a balance between making the book accessible to a wider audience while simultaneously making it challenging and rigorous, subjecting the art of strategy to a scientific inquiry. The book provides a very pragmatic and intuitive, yet rigorous approach to strategy formulation. We owe an intellectual debt to the many scholars who made significant contributions to the related literatures and to the many individuals who provided us with generous comments, criticism, ideas, and suggestions. We thank Avinash Dixit for his feedback, prologue, and encouragement. Marcel Boyer, Marco Días, Kuno Huisman, Grzegorz Pawlina, Sigbjørn Sødal, and Jacco Thijssen made invaluable detailed comments and provided suggestions for improvement on the entire manuscript. Several other colleagues offered valuable comments on select parts of the book or on specific chapters: Christoph Flath, John Khawam, Bart Lambrecht, Richard Ruble, and Bruno Versaevel. We also thank Stefan Hirth, Helena Pinto, Artur Rodrigues, and Han Smit for useful comments. We thank Robert J. Aumann, Rainer Brosch, Peter Damisch, Marco Días, Avinash Dixit, Eric Lamarre, Scott Mathews, Robert C. Merton, Reinhard Selten, and Jean Tirole for enhancing the relevance of this book with their valuable interviews and comments. The authors would like to thank Arnd Huchzermeier for his support. Jane MacDonald from the MIT Press has been most enthusiastic, efficient, and supportive. Last but not least, we would like to thank our families, alive or departed, for their love and support. 1. Chapter 12 can be thought of as the climax of the book, but we need to introduce various key notions (e.g., investment trigger, open-loop vs. closed-loop equilibrium) in a step-bystep fashion to facilitate and smooth out the exposition. Several of the issues left unresolved in the earlier parts of the book are addressed in chapter 12.
1
The Strategy Challenge
At a time when national monopolies have been losing their secular wellprotected positions owing to market liberalization in the European Union and elsewhere across the globe, strategic interdependencies and interactions have become a key challenge for managers in many corporations. Strategic questions abound: How should a firm sustain or gain market share? How to differentiate oneself from others in the grueling global marketplace? When precisely should a firm enter or exit an industry when it faces uncertainty and significant entry and exit costs? Recent developments in economics, finance, and strategy equip management facing such challenges with a concrete framework and tool kit on how to behave strategically in such a complex and changing business environment. Corporate finance and game theory provide complementary perspectives and insights regarding strategic decision-making in business and daily life. Box 1.1 motivates the relevance of game theory to the understanding of daily life situations. The option games approach followed in this book paves the way for a more rigorous approach to strategy formulation in many contexts. It helps integrate in a common, consistent framework the recent advances made in these diverse disciplines, providing powerful insights into how firms should behave in a dynamic, competitive and uncertain marketplace. We first highlight in section 1.1 several environmental factors that justify why firms should be careful when formulating their corporate strategies in an uncertain, competitive business environment. In section 1.2 we discuss how an understanding of competitive strategy in terms of sound economic principles is useful to managers. Two complementary but separate perspectives on strategy (corporate finance and game theory) are discussed in section 1.3. We address the need for an integrative approach to corporate strategy in section 1.4. We provide an overview of the book organization in section 1.5.
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Box 1.1 Game theory in daily life
All Is Fair in Love, War, and Poker Tim Harford, BBC News Online What Do Love, War, and Poker Have in Common? High stakes, perhaps. Certainly, in all three you spend a lot of effort trying to work out what the other side is really thinking. There is another similarity: economists think they understand all three of them, using a method called “game theory.” Threats and Counterthreats Game theory has been used by world champion poker players and by military strategists during the cold war. Real enthusiasts think it can be used to understand dating, too. The theory was developed during the Second World War by John von Neumann, a mathematician, and Oskar Morgenstern, an economist. Mr. von Neumann was renowned as the smartest man on the planet—no small feat, given that he shared a campus with Albert Einstein—and he believed that the theory could be used to understand cold war problems such as deterrence. His followers tried to understand how a nuclear war would work without having to fight one, and what sort of threats and counterthreats would prevent the US and the Soviets bombing us all into oblivion. Since the cold war ended without a nuclear exchange, they can claim some success. Understand the World Another success for game theory came in 2000, when a keen game theorist called Chris “Jesus” Ferguson combined modern computing power with Mr. von Neumann’s ideas on how to play poker. Mr. Ferguson worked out strategies for every occasion on the table. He beat the best players in the world and walked away with the title of world champion, and has since become one of the most successful players in the game’s history. Game theory is a versatile tool. It can be used to analyze any situation where more than one person is involved, and where each side’s actions influence and are influenced by the other side’s actions. Politics, finding a job, negotiating rent, or deciding to go on strike are all situations that economists try to understand using game theory. So, too, are corporate takeovers, auctions, and pricing strategies on the high street. Financial Commitment But of all human interactions, what could be more important than love? The economist using game theory cannot pretend to hand out advice on
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Box 1.1 (continued)
snappy dressing or how to satisfy your lover in the bedroom, but he can fill some important gaps in many people’s love lives: how to signal confidence on a date, or how to persuade someone that you are serious about them, and just as importantly, how to work out whether someone is serious about you. The custom of giving engagement rings, for instance, arose in the United States in the 1930s when men were having trouble proving they could be trusted. It was not uncommon even then for couples to sleep together after they became engaged but before marriage, but that was a big risk for the woman. If her fiancé broke off the engagement she could be left without prospects of another marriage. For a long time the courts used to allow women to sue for “breach of promise” and that gave them some security, but when the courts stopped doing so, both men and women had a problem. They did not want to wait until they got married, but unless the man could reassure his future wife, then sleeping together was a no-no. The solution was the engagement ring, which the girl kept if the engagement was broken off. An expensive engagement ring was a strong incentive for the man to stick around—and financial compensation if he did not. Not Committed Modern lovers might think the idea of engagement ring as guarantee is a thing of the past, but they can still use game theory to size up their partners. When a couple with separate homes move in together, selling the second home is an important signal of commitment. That second home is an escape route—valuable only if the relationship is shaky. If your partner wants to hang on to his bachelor pad, do not let him tell you it is merely a financial investment. Game theory tells you that he is up to something. Reprinted with permission of BBC News, bbc.co.uk/news. Publication date: August 17, 2006.
1.1 The Changing Corporate Environment The competitive environment around the globe is becoming increasingly challenging for managers as modern economies have witnessed tremendous changes over the last three decades. In this constantly evolving environment, where firms must often make quick decisions that have long-term impact, it is anybody’s guess what might happen in the future— market developments often prove expectations wrong. Firms must carefully commit to specific strategies while developing adaptive capabilities
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in an ever-changing marketplace. Globalization, deregulation, and the emergence of new economies (e.g., Brazil, Russia, India, and China) have created both threats and opportunities for incumbent firms who now have to adapt more effectively to the rapidly changing global environment or suffer damage by new entrants and risk extinction. Following the liberalization and deregulation of European economies, only a limited number of industries have remained secure, while most companies across the board face serious competitive pressures. At the same time other economic sectors traditionally characterized by a large number of companies have undergone significant consolidation, resulting in oligopoly structures with a reduced number of players. The recent economic crisis has amplified these consolidation pressures. The mining giant Rio Tinto has recently merged with BHP Billiton, forming a virtual duopoly together with Brazilian mining giant Vale. M&A deals have similarly reshaped the automotive sector, with the recent acquisitions of Chrysler by Fiat, of Porsche and Suzuki by Volkswagen, and of Mitsubishi by PSA (Peugeot, Citroën). British Airways together with Iberia claim the top two position in the fiercely competitive European airline business. In the United States, the merger between United Airlines and Continental Airlines created one of the world’s biggest airlines. A dramatic concentration has also taken place in the banking sector: out of the top five investment banks worldwide, only two (Goldman Sachs and Morgan Stanley) have remained independent. Notable banking deals include the acquisition of Washington Mutual by JP Morgan Chase, of Countrywide by Bank of America, and of the Belgian bank Fortis by BNP Paribas. These two concurrent phenomena—liberalization and consolidation— have put higher on the corporate agenda the assessment of strategic uncertainty. Italy’s dominant state electricity authority, Enel, is a good case in point. Just a decade ago Enel was in a very comfortable position, enjoying an established natural monopoly in the Italian electricity market with the benediction of the national government. The main concern for Enel during this period was to minimize or keep under control its mix of input or production costs, as the output electricity price was regulated. Several years later Enel lost its preferential monopoly position due to the liberalization of the European markets, and the competitive environment facing Enel changed dramatically. Enel was forced to sell half of its generating assets to half a dozen smaller local rivals, creating more competition in its home base. Electricity price deregulation accompanied by oil and other fuel price fluctuations have added considerable pressure
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and uncertainty for Enel. With further deregulation other European competitors (e.g., the dominant electricity producer of France, EDF) also entered the Italian electricity market. Today Enel has to consider the actions of local as well as international competitors on the national soil, as well as contemplate investing itself in new or emerging markets, such as Russia, to sustain or leverage its once dominant position in the area. Just as its European and global counterparts, Enel now faces a broader range of uncertainties and challenges: How to cope with increased energy (input) and electricity demand (output) uncertainties? How to compete with local and global rivals in an ever-changing local and global competitive landscape? How to assure and diversify its energy portfolio mix in a globalized marketplace? How to formulate and dynamically adjust its strategy, knowing when to compete, threaten, bargain, or cooperate with its rivals? These are the kind of questions we will be addressing in this book. Many situations corporate managers face today are characterized by both market and strategic uncertainty with respect to the economic environment. When one desires to address such complex issues, some simplification of reality is useful to focus on the fundamental trade-offs. Some simple models in management are being revised as they offer rather simplistic approaches that no longer describe current economic reality. The field of investment under uncertainty falls in this category. Prevailing management approaches often lead to investment decisions detrimental to the overall firm’s long-term well-being. In an increasingly uncertain and competitive environment, corporate managers need appropriate management tools that can provide long-term guidance. This book describes a novel approach aimed to enable managers make rational decisions in a competitive environment under uncertainty. It allows managers to quantify and balance the conflicting impacts of managerial flexibility and the strategic value of early investment commitment in influencing rivals’ strategic behavior. Real options analysis is widely considered to be more reflective of reality than traditional financial methods (e.g., net present value) in that it takes managerial flexibility into account.1 To avoid dealing with complex models, however, standard real options analysis often ignores 1. Investment under uncertainty is part of the mainstream literature on finance, economics, and strategic management. In the last decades financial theory has been supplemented with real options analysis. Use of the financial options analogue can be insightful in assessing flexibility embedded in real asset situations. The real options approach to investment has reached a corporate finance textbook status and is currently applied in leading corporations for guiding real-world strategic investment decisions.
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the strategic interactions among option holders, analyzing investment decisions as if the option holder has a proprietary right to exercise. This simplifying perspective is far from being realistic in many situations because firms generally compete with rivals. Several firms may share an option in the industry and hence option exercise strategies cannot be formulated in isolation. Rather, optimal investment behaviors must be determined as part of an industry equilibrium. As a consequence of this more pragmatic view of the nature of the competitive environment, a new theory called “option games” has emerged. This theory combines the concepts and tools offered by traditional real options analysis with game theory principles designed to help figure out how players behave in strategic conflict situations. The option games approach we elaborate on herein provides powerful insights into understanding strategic interactions and challenges traditional thinking that presumes that firms pursue strategies in isolation. Game theory is for the most part deterministic. Option games help management better intuit how uncertainty can be modeled in a strategic setting. This approach helps improve prediction and understanding of industry dynamics in highly uncertain industries. It enhances previous industrial organization literature on strategic investment in a deterministic setting to better explain the strategic investment behavior of firms under changing conditions. Box 1.2 provides an overview of the challenges commercial airframe manufacturers Box 1.2 Evolving strategy in commercial aviation
Boeing Bets the House on Its 787 Dreamliner Leslie Wayne In recent years Boeing has stumbled badly, ceding its decades-long dominance in commercial aviation to Airbus and becoming mired in a string of scandals over Pentagon contracts. The terrorist attacks of 2001 depressed demand at a time when the company’s product line paled against appealing new planes from Airbus. In one year alone, from 2001 to 2002, Boeing’s profits dropped 80 percent. But the view from Seattle, the headquarters of Boeing’s commercial jet operations, has more of that Chinese pep-rally spirit than such gloomy talk might indicate . . .. With revenue having grown for the second consecutive year, to $54.8 billion in 2005, and a record number of orders on its books, Boeing has had a huge gain in its stock price—to more than $80 a share, more than three times its nadir of $25 in 2003. Boeing’s 1,002 orders last
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Box 1.2 (continued)
year fell short of Airbus’s 1,055. But Boeing’s orders included more widebody planes, which analysts valued at $10 billion to $15 billion more than Airbus’s. But what is really driving the high spirits at Boeing—and the high stock price—is a plane that has not yet taken to the skies: the 787. It is Boeing’s first new commercial airplane in a decade. Even though it will not go into service until 2008, its first three years of production are already sold out— with 60 of the 345 planes on order going to China, a $7.2 billion deal. Other big orders have come from Qantas Airways, All Nippon Airways, Japan Airlines, and Northwest Airlines. Big orders mean big money, of course—and that is good, because analysts estimate that Boeing and its partners will invest $8 billion to develop the 787. Boeing is also risking a new way of doing business and a new way of building airplanes: farming out production of most major components to other companies, many outside the United States, and using a carbonfiber composite material in place of aluminum for about half of each plane. If it works, Boeing could vault back in front of Airbus, perhaps decisively. If it fails, Boeing could be relegated to the status of a permanent also-ran, having badly miscalculated the future of commercial aviation and unable to meet the changing needs of its customers. “The entire company is riding on the wings of the 787 Dreamliner,” said Loren B. Thompson, an aviation expert at the Lexington Institute, a research and lobbying group in Arlington, Virginia, that focuses on the aerospace and military industries. “It’s the most complicated plane ever.” Boeing calls the 787 Dreamliner a “game changer,” with a radically different approach to aircraft design that it says will transform aviation. A lightweight one-piece carbon-fiber fuselage, for instance, replaces 1,200 sheets of aluminum and 40,000 rivets, and is about 15 percent lighter. The extensive use of composites, already used to a lesser extent in many other jets, helps improve fuel efficiency. To convince potential customers of the benefits of composite—similar to the material used to make golf clubs and tennis rackets—Boeing gives them hammers to bang against an aluminum panel, which dents, and against a composite one, which does not. At the same time, the 787 has new engines with bigger fans that are expected to let the plane sip 20 percent less fuel per mile than similarly sized twin-engine planes, like Boeing’s own 767 and many from Airbus. This is no small sales point, with oil fetching around $70 a barrel and many airlines struggling to make a profit even as they pack more passengers into their planes. “The 787 is the most successful new launch of a plane—ever,” said Howard A. Rubel, an aerospace analyst at Jeffries & Company, an investment bank that has advised a Boeing subsidiary . . .. The 787 is designed to carry 220 to 300 people on routes from North America to Europe and Asia. Boeing is counting on it to replace the workhorse 767, which is being
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Box 1.2 (continued)
phased out, and, it hopes, a few Airbus models as well. Its advantages go beyond fuel efficiency: Boeing designed the 787 to fly long distances while keeping passengers relatively comfortable. That approach grows out of another gamble by Boeing—that the future of the airline business will be in point-to-point nonstop flights with medium-size planes rather than the current hub-and-spoke model favored by Airbus, which is developing the 550-seat A380 superjumbo as its premier long-haul jetliner. Flying point to point eliminates the need for most passengers to change planes, a competitive advantage so long as the Dreamliner is as comfortable and as fast as a bigger aircraft. And after talking with passengers around the world, Boeing designed the 787 to have higher humidity and more headroom than other airplanes, and to provide the largest windows of any commercial plane flying today. “We are trying to reconnect passengers to the flying experience,” said Kenneth G. Price, a Boeing fleet revenue analyst. With airlines squeezing every last cent and cutting back service, “flying is not enjoyable,” Mr. Price said. “Every culture fantasizes about flying,” he added. “All superheroes fly. But we were taking a magical experience and beating the magic out.” Even more innovative for Boeing is the way it makes the 787. Most of the design and construction, along with up to 40 percent of the estimated $8 billion in development costs, is being outsourced to subcontractors in six other countries and hundreds of suppliers around the world. Mitsubishi of Japan, for example, is making the wings, a particularly complex task that Boeing always reserved for itself. Messier-Dowty of France is making the landing gear and Latecoere the doors. Alenia Aeronautica of Italy was given parts of the fuselage and tail. Nor are these foreign suppliers simply building to Boeing specifications. Instead, they are being given the freedom, and the responsibility, to design the components and to raise billions of dollars in development costs that are usually shouldered by Boeing. This transformation did not come overnight, of course, nor did it begin spontaneously. Boeing changed because it had to, analysts said. “Starting in 2000, Airbus was doing well,” said Richard L. Aboulafia, an aerospace analyst with the Teal Group, an aviation research firm in Fairfax, Va. “Boeing had to reconsider how it did business. That led to the framework for the 787—getting the development risk off the books of Boeing and coming up with a killer application.” Boeing plans to bring the 787 to market in four and a half years, which is 16 to 18 months faster than most other models. All of that is good, Mr. Aboulafia added, if it works. It is a tall order for a wholly new plane being built with new materials, many from new suppliers and assembled in a new way. “The 787 is operating on an aggressive timetable and with aggressive performance goals,” he said. “It leaves no margin for error.”
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Box 1.2 (continued)
Never before has Boeing farmed out so much work to so many partners—and in so many countries. The outsourcing is so extensive that Boeing acknowledges it has no idea how many people around the world are working on the 787 project. Airbus, Boeing’s sole rival in making big commercial airliners, is also making a big bet on the future, but in a different direction. The companies agree that in 20 years, the commercial aviation market may double, with today’s big orders from China, India, and the Middle East to be followed by increased sales to American and European carriers as they reorganize and reduce costs. By 2024, Boeing estimates, 35,000 commercial planes will be flying, more than twice the number now aloft, and 26,000 new planes will be needed to satisfy additional demand and replace aging ones. But how passengers will get from place to place, and in what planes, will depend on whether Boeing or Airbus has correctly forecast the future. Boeing believes that passengers will want more frequent nonstop flights between major destinations—what the industry calls “city pairs.” That is what led to the big bet on the Dreamliner, a midsize wide-body plane that can fly nonstop between almost any two global cities—say, Boston to Athens, or Seattle to Osaka—and go such long distances at a lower cost than other aircraft. Airbus believes that airplane size is more important than frequent nonstop flights and that passengers will stick with a hub-and-spoke system in which a passenger in, say, Seattle, will fly to Los Angeles and transfer to an Airbus 380 to go to Tokyo before catching a smaller plane to Osaka. That view has led it to spend $12 billion to develop the double-deck A380, the largest passenger jet ever—a bet that is as crucial to its future as the 787 is to Boeing’s. “We have a fundamental difference with Airbus on how airlines will accommodate growth,” said Randolph S. Baseler, Boeing’s vice president for marketing. “They are predicting flat growth in city pairs. We are saying that people want more frequent nonstop flights. They believe airplane size will increase, and we believe that airplanes will not increase in size that much. Those two different market scenarios lead to two different product strategies.” The market, of course, will determine the winner, but given the industry’s long lead times, that may not be clear for 10 to 20 years. For now, airlines have ordered 159 copies of the A380—which has a list price of $295 million and is scheduled to enter service this year—and more than twice as many 787s, which list for $130 million and are scheduled to enter service in two years. Publication date: May 7, 2006.
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have faced over the recent years, focusing on the changes in corporate strategy of Boeing compared with Airbus. A manager from Boeing discusses in box 1.3 the use of real options to capture and assess the diverse sources of uncertainty in his business; an analysis of the strategic interplay vis-à-vis Airbus is highlighted. 1.2 What Is Strategy? Corporate strategy is high on the agenda of every major corporation.2 The strategy a firm formulates and how it implements that strategy will determine to a large extent whether it will survive and be successful in the marketplace or become extinct. Formulating the right strategy in the right place at the right time is not an easy task. It requires deep analysis and ready-to-implement, adaptable solution programs. Our approach to strategy is based on the premise that strategic management is a structured, rational discipline relying on rigorous market and competitive analysis. One can understand why firms succeed or fail by analyzing their decision processes in terms of consistent principles of market economics and rational strategic actions. This is the reason why a large literature in strategic management relies on economic theory as it provides a reliable, rigorous foundation to understanding specific developments and reactions taking place in the market place. Good firm performance is considered the result of soundly formulated and well-implemented strategies. Grant (2005) identifies the following elements as key to a successful strategy: (1) simple, agreed-upon, longterm objectives; (2) deep understanding of the competitive landscape; (3) objective appraisal of the firm’s internal resources and capabilities; and (4) effective implementation. There are no easy “recipes for success” applicable to each firm in every industry.3 The pursuit of one-size-fits-all 2. Although it is commonly agreed that strategy is critical in today’s changing corporate environment, there is no universally agreed-upon definition of business strategy. According to Chandler (1962), strategy is “the determination of the basic long-term goals and objectives of an enterprise and the adoption of courses of action and the allocation of resources necessary for carrying out these goals.” According to Mintzberg et al. (2002), strategy is “the pattern or plan that integrates an organization’s goals, policies and action sequences into a cohesive whole.” 3. There are two main approaches to strategy formulation. The first approach looks at specific firms or case studies examining why these firms are successful and tries to deduce success factors that might be applicable to other firms. This is the “best-practices” approach. Herein we take a different approach. We formulate a conceptual framework for strategic management and assess if it provides prescriptive insights into real-world managerial problems.
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Box 1.3 Interview with Scott Matthews, Boeing
1. Do you believe real options is more suitable than other capital budgeting approaches to provide managerial guidance? Where and to what extent is real options analysis used at Boeing? Real options provides a more informed decision for our strategic projects. Of special significance are the scenarios that we build around the real options analyses that help us understand both the risks and the opportunities of any venture. To date, real options analysis has been used mostly on large-scale projects. Because of the higher investment amounts, these large projects pose particular risks that require more careful analyses including the use of real options techniques. 2. What are the sources of uncertainty you face at Boeing, and how do you manage them with options, physical or contractual? Boeing projects have many sources of uncertainty. We build models that attempt to integrate technology development, design, manufacture, supply chain, and market forces, including possible actions of our competitors. These models have dozens, even hundreds, of variables modeled using various Monte Carlo and discrete event simulation capabilities. Usually there are just a handful of principal uncertainty drivers which are determined using sensitivity analysis. We then apply a series of targeted investments to investigate and better understand the true scale of these uncertainties. Since the uncertainty landscape is in constant evolution, due to both our risk mitigation efforts as well as exogenous events, we continue to update the model and modify our investments as appropriate. These are
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Box 1.3 (continued)
often modeled as real option investments with a type of varying volatility, as we are attempting to both reduce uncertainty while at the same time increase the value of the subsequent project stage. 3. Do you see a usefulness for game theory and option games in Boeing’s strategic thinking, for example, vis-à-vis Airbus? We find that game-theoretical approaches provide additional insight to a solution set as long as the number of actors is limited to just a few players. At a certain point market considerations dominate and provide a better approach to modeling the scenarios. We have managed to execute a few plays against our competitors, the origin of which could be traced back to strategic gaming scenarios and market timing. When we are successful, these plays are often highly leveraged, and therefore take on the characteristics of well-placed option investments. However, like other companies in dynamic markets, we find our competitive response limited by timing or technology and product availability considerations.
success factors has failed to provide a coherent direction to guide the actions and decisions of firms. Ghemawat (1991) criticizes the success factors approach and identifies commitment as a main driver of corporate success or failure. He sees commitment as a well-thought-out plan of action affecting the firm in the long term. A successful strategy should exhibit consistent but adaptive behavior over time and involve certain strategic commitments that might sometimes hinder managerial flexibility. This feature of strategy implies that, in an uncertain environment, resolving the investment or commitment timing issue is critical to a firm’s success. A firm should not always invest or commit immediately, but should be prepared to decide at the right moment to reap the benefits of a developing opportunity. As suggested by Dixit and Pindyck (1994), investment situations where decisions are costly or impossible to reverse compel corporate managers to be cautious and careful to make decisions at the right time. Strategy should also be dynamic in that it should be adaptable to changing market circumstances or competitive dynamics. Following Rumelt (1984, p. 569), the essence of competitive strategy is being in “constant search for ways in which the firm’s unique resources can be redeployed in changing circumstances.” The increasing cone of market and strategic (competitive) uncertainty makes the dynamic formulation of strategy key to survival and success in a changing marketplace. Box 1.4
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Box 1.4 Flexible strategy and real options
Stay Loose: By Breaking Decisions into Stages, Executives Can Build Flexibility into Their Plans Lenos Trigeorgis, Rainer Brosch, and Han Smit, Wall Street Journal In turbulent times adaptability is critical. That’s why today flexibility is more valuable than ever in business strategy. Markets, technologies, and competition are becoming more dynamic by the day. To succeed in this environment, companies need to position themselves to capitalize on opportunities as they emerge, while limiting the damage if adversity hits. This requires a whole new level of flexibility. Good managers have always been able to think on their feet. But many widely applied tools of strategy development were designed for relatively stable environments. As a result business strategy may too often lock managers into decisions that turn out to be flawed because something outside their control doesn’t go as planned. What is needed is a systematic translation of managers’ flexibility into strategy—a plan that lays out a series of options for managers to pursue or decline as developments warrant. That is the essence of what is known as “real options” analysis, an approach that borrows from the workings of the financial markets. Just as stock options, for instance, give the holder the right, but not the obligation, to buy or sell shares at a given price at some time in the future, real options give executives the right, but not the obligation, to pursue certain business initiatives. Start Small Instead of making rock-hard plans and irreversible long-term commitments, the idea is to create flexibility by breaking decisions down into stages. When building a new plant, for example, it may be tempting to realize the full economies of scale by building the biggest facility the company can manage. But it may be wiser to first build a smaller plant that can be easily expanded later on. That way, if the market for the products the plant produces should decline, a smaller investment has been put at risk. At that point managers have the option to scale down or abandon operations. On the other hand, if things turn out well, they have the option to expand the plant. As a mind-set this approach encourages managers to be flexible in their planning. In more concrete terms it allows them to value investment decisions and business initiatives in a new way. Instead of making a decision based on a rigid financial analysis of a given project as a whole, managers can analyze, from the start, the financial implications of each step along the way and every potential variation—without committing to anything
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Box 1.4 (continued)
before they must. Once the project is under way, they also can account for the changing value of each option as events unfold. All that information gives them a clearer framework for decisions on whether to launch a project and whether to proceed, hold back, or retreat at each stage. What does this look like in practice? A leading European automaker was considering two investment alternatives for the production of a new vehicle. Under one alternative, production would be based entirely in one country. Under the other, the company would set up plants around the world, allowing it to switch production from site to site to take advantage of fluctuations in exchange rates or labor costs. The cost of the flexible system would be higher. But the company decided that the value of that flexibility, with its promise of cost savings and increased profits, exceeded the difference in cost between the two alternatives. So it chose the multinational plan. Competitive Edge Real options analysis can also be useful in helping strategic planners address the challenges of competition. Many managers already incorporate game theory into their planning to help predict how competition will play out. But with competition emerging and evolving more rapidly than ever, supplementing game theory with real options analysis can help companies be more flexible in how they react. Consider, for example, the question of whether a company should aim to preempt competition or choose to cooperate with other players in a way that could expand the market. This is a question of growing relevance as sometimes competing technologies are at the heart of more products. In deciding whether to fight or cooperate, companies can use real options analysis to better quantify the value of each contingency, including the value of the options that would be lost or gained depending on what competitive course is chosen. What this all adds up to is a portfolio of corporate real options, each with a value that will change along with the company’s developing markets. Those who manage that portfolio most effectively will be in the best position to realize their company’s growth potential. Reprinted with permission of The Wall Street Journal, Copyright © 2007 Dow Jones & Company, Inc. Publication date: September 15, 2007.
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discusses the need for strategic plans to be flexible and adaptable in a changing environment. Since strategic decisions have long-term consequences, one should look not only at today’s advantages or drawbacks but also at the long-term consequences and value of such decisions. The trade-off between the benefits of commitment (as part of a consistent strategy over time) and remaining flexible and adaptive to changing circumstances calls for an integrative approach weighing the merits of flexibility against commitment. 1.3 Two Complementary Perspectives on Strategy Two approaches to strategy are of particular interest as they provide insights that help management deal with the flexibility or commitment trade-off: corporate finance and game theory. These disciplines are generally considered separate but are in fact complementary. We discuss each one next. 1.3.1
Corporate Finance and Strategy
At first sight the link between corporate finance and strategy may not be that clear. Within corporations, finance is in charge of raising firm resources, while the strategy department is concerned with how to allocate these resources strategically. The two departments deal, however, with two sides of the same coin. Financial managers are concerned with how to finance a project at a reasonable cost. They are aware that resource providers (e.g., shareholders or banks) will carefully scrutinize what the firm plans to do with the resources they are asked to provide, carefully assessing the firm’s strategic plans and the quality of its management. The formed opinion of the resource providers will influence the cost of the resources the firm has access to. A good financial manager cannot therefore ignore the firm’s strategy. Understanding and communicating the firm’s strategy should be one of her primary tasks. Following a finance theory approach, the objective of the firm is to maximize the wealth (utility) of shareholders. According to the Fisher separation theorem, this objective is achieved when maximizing the firm’s market value. A fundamental question in corporate finance is how to attain this objective. As part of corporate finance, capital budgeting considers this problem from an investment perspective, being concerned with the optimal allocation of scarce resources among alternative
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projects.4 A key issue is how to address the intertemporal trade-off faced by a firm between paying more dividends or cash distributions now and investing in growth projects meant to generate future cash flows. The established criterion in capital budgeting is the discounted cashflow (DCF) or net present value (NPV) method. The approach involves a relatively easy-to-understand logic and methodology that consists in assessing the current value of a project based on the expected future cash flows it will generate, net of the related costs. Management estimates the stream of future expected cash flows over the project’s life and discounts them back to the present using a risk-adjusted discount rate, obtaining the project’s present value V . It then subtracts the (present value of) investment outlays, I , obtaining the current (t = 0) net present value:5 NPV = V − I .
(1.1)
Alternatively, the present value represents the discounted sum of economic profits.6 The economic profit in a given period represents the firm’s total revenue earned in that period minus all relevant opportunity costs, including the cost of capital. Following the DCF or NPV paradigm, the firm creates shareholder value by following the NPV rule, prescribing to immediately undertake projects with positive NPV, meaning NPV > 0 or V > I . In the absence of managerial flexibility, net present value is the main valuation measure consistent with the firm’s objective to maximize shareholders’ wealth.7 Other valuation measures, such as payback period, accounting rate of return, or internal rate of return are considered inferior to NPV and sometimes even inconsistent. The above finance theory often appears rather technical and not so relevant for strategic management practice. Already in Myers (1984), a 4. Corporate finance provides a useful frame to help managers make investment and financing decisions. Two subfields of corporate finance are particularly relevant for corporate managers: capital budgeting, or how to make investment decisions, and financing, or how to finance projects at the lowest cost available. It is commonly agreed that real investments are more important for creating shareholder value than financial engineering. 5. Consider a project generating over its lifetime (T years) expected cash inflows E [ Rt ] in each future year t. Launching the project is costly, involving expected cash outflows t t E [Ct ]. Let V ≡ ∑Tt =0 E [Rt ] (1 + k ) and I ≡ ∑Tt =0 E [Ct ] (1 + k ) denote the present value of the stream of cash inflows and outflows, respectively. k denotes the appropriate riskadjusted discount rate. The necessary cash outflow I might be a single investment outlay incurred at the outset or the present value of a series of outflows. 6. The economic value added (EVA) approach is based on this notion. 7. Throughout the book, we ignore agency problems inside a firm that may invalidate the NPV rule. Myers (1977) discusses the problem of “underinvestment.” Managers acting in the shareholders’ interest may reject projects with positive NPV when the firm is close to bankruptcy since investing in these projects would only benefit debt-holders.
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gap between finance and strategy was identified. Myers offers three main explanations for this gap: NPV is often mistakenly applied Firms in practice often pursue financial objectives that are inconsistent with basic financial theory. They may focus on short-term results rather than long-term value creation. For instance, firms may worry about the impact of their strategic decisions on today’s P&L and on today’s balance sheet.8 Financial theory in fact stresses the importance of taking a long-term perspective to enhancing firm value over short-term creative accounting.9 The balance sheet or income statement are accounting instruments presenting snapshots of the moment or period and do not necessarily mirror real long-term value creation. Another pitfall is that some managers may pursue corporate diversification to reduce total risk for their own benefit.10 In addition managers often treat available divisional resources as being limited. This internally imposed constraint is in sharp contrast with the basic finance assumption that firms have ready access to capital markets at the prevailing cost of capital. Even if acquiring new financial resources may be more costly, the project should be adopted if the project brings more value than it costs to undertake it. •
Finance and strategy mind-sets differ They represent two cultures looking at the same problem. In perfect competition the firm presumably makes no excess economic profit. Strategists are thus looking for deviations from perfect competition to generate excess profits. Such deviations result from distinctive sustainable competitive advantages. Given the linkage between competitive advantage and excess economic profits, strategists often find it superfluous to determine the net present value (as the discounted sum of economic profits) once they have identified the source of competitive advantages. •
NPV has limited applicability The DCF approach involves the estimation of a risk-adjusted discount rate, a forecast of expected cash flows, and an assessment of potential side effects (e.g., erosion or synergies between projects) or time-series links between projects. The last aspect is most difficult to handle with traditional techniques because
•
8. Other common mistakes include the inconsistent treatment of inflation (deflated cash flows discounted back at a discount rate assuming inflation) and unrealistic hurdle rates (use of discount rates that take into account both systematic and diversifiable risk). 9. Short-term orientation is allegedly rampant in countries relying heavily on the capital markets. 10. Risk reduction through portfolio diversification had better be undertaken by investors directly in the capital markets; corporate diversification undertaken by managers is a less efficient means to diversify risk.
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today’s investment decisions may constrain or open up new future opportunities. NPV has other drawbacks. First, the NPV paradigm views investment opportunities as now-or-never decisions under passive management. This precludes the possibility to adjust future decisions to unexpected future developments in industry cycles, demand, or prices. Firms need to position themselves to capitalize on opportunities as they emerge while limiting the damage arising from adverse circumstances. If market developments deviate significantly from the expected future scenario, managers can generally revise their future decisions to protect themselves from adverse downward movements or tap on favorable developments and further growth potential. Applying the NPV rule strictly is ill-advised when managers can adjust their planned investment programs or delay and stage their investment decisions. Managers following the prescriptions offered by NPV may find themselves locked into decisions that are flawed when something outside their control does not go as planned. Second, NPV typically assumes a constant discount rate for each future time and state scenario regardless of whether the situation is favorable. Table 1.1 summarizes situations where NPV might give a good approximation of reality and when it might be misleading. Finally, NPV typically overlooks the consequences of competitive actions. Strategy is in need of a quantitative tool that allows for dynamic consideration of changing circumstances. Academics have attempted early on to use alternative approaches to overcome the problems inherent in NPV, particularly to deal with uncertainty and the dynamic nature of investment decision-making. Such methods include sensitivity analysis, Table 1.1 Use of NPV for financial and corporate real assets
Appropriate
Inappropriate
Financial assets
Corporate real assets
Valuation of bonds, preferred stocks, and fixed-income securities Valuation of relatively safe stocks paying regular dividends
Valuation of flows from financial leases
Valuation of companies with significant growth opportunities Valuation of call and put options
Source: Myers (1984, p. 135).
Valuation of “cash cows” Valuation of projects with substantial growth opportunities Valuation of R&D projects
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simulation, and decision-tree analysis. Each has had, however, known drawbacks. Sensitivity analysis considers each variable in isolation, thereby ignoring correlations among them. Simulation (e.g., Monte Carlo) faces the same risk-adjustment (discount rate) problem as NPV and generally requires additional adjustment to handle certain recursive problems (e.g., American options) because it is a forward-looking process. Sensitivity analysis and standard simulation are static approaches in that they assume that management is precommitted to a previously agreedupon course of action. In real life this hardly holds. Managers have valuable flexibility and can adapt to the actual market developments once uncertainty gets resolved. They may, for example, abandon a onceundertaken project if the prospects prove gloomy. Managers may also have other options to alter project features in view of the actual market development. Decision-tree analysis (DTA) can be seen as a refined version of NPV aimed to take into consideration the dynamic nature and across-time linkages of decision-making. DTA attributes probabilities to different states of the world and determines in each case the strategy management should optimally formulate (e.g., increase the production scale, switch off the plant, exit the market). In this respect DTA considers the options management has and provides better insights on the dynamic structure of the problem. Trigeorgis and Mason (1987) point out that DTA fails, however, to be economically sound. The discount rate might not be constant over time or across states as typically assumed. Determining the real probabilities of occurrence of each state is often quite involved. Real options analysis (ROA) is an enhanced version of decision-tree analysis that provides improvement in terms of risk-adjustment and determination of probabilities.11 This enhancement is the result of using insights from option-pricing theory.12 Just as stock options give the holder the right, but not the obligation, to buy or sell shares at a given price at some time in the future, real options gives executives the right, but not the obligation, to pursue certain business initiatives. ROA is operationally similar to DTA, with the key difference that the probabilities are riskadjusted, which allows the use of the risk-free discount rate. Real options 11. Several corporate-finance textbooks subsume DTA into real options analysis. We disagree. Real options is the application of option-pricing theory (contingent claims analysis) and risk-neutral pricing to real investment situations (Myers 1977). 12. An alternative to NPV for risk-adjusting risky future cash flows is to consider the certainty equivalents of the uncertain future cash flows and discount them at the risk-free rate. This alternative approach for risk adjustment is a cornerstone of risk-neutral pricing and real options analysis.
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analysis is an innovative capital-budgeting tool suitable for the analysis of dynamic decision making under exogenous uncertainty. It enables quantifying strategic considerations that justify sometimes undertaking projects with negative (static) net present value or delaying projects with positive NPV. Box 1.5 highlights the challenges managers face under uncertainty, the inability of NPV to cope with them, and the usefulness of real options in practice. 1.3.2
Game Theory and Strategy
Corporate life would be rather comfortable if a single firm were the only one operating in the marketplace. As a monopolist the firm could Box 1.5 Uncertainty, NPV, and real options in practice
Getting Real: Want to Take More Uncertainty out of Capital Investment Decisions? Try Real Options S. L. Mintz, CFO Magazine “The Edsel is here to stay.” That’s what Ford Motor Co. chairman Henry Ford II told Ford dealers in 1957. “There is no reason why anyone would want a computer in their home.” Thus intoned Digital Equipment Corp. founder Kenneth Olsen in 1977. Even for business leaders with vision, the future is difficult to predict. So where does that leave less-than-legendary executives come budget-planning season? Stuck, largely, with the same venerable tools that guided their predecessors and their predecessors: net present value and gut instinct. Short of denigrating tools that account for many great successes (along with memorable flubs), many executives are wondering if that’s all there is. “There is definitely room for improvement,” concedes Rens Buchwaldt, CFO of Bell & Howell Publishing Services, in Cleveland. Large capitalinvestment decisions—whether it’s launching a new automobile, or building a chip-fabrication plant, or installing an ERP system, or making any number of other very pricey investments—hurl companies toward uncertain outcomes. Huge sums are at risk, in a competitive climate that demands ever-faster decisions. Is there a better way to evaluate capital investments? A growing and vocal cadre of academics, consultants, and CFOs say there is one: real options. Fans insist that real options analysis extends quantitative rigor beyond discount rates and expected cash flows. “Everybody knew there was some kind of embedded value” in strategic options, says an oil industry finance
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executive. Real options analysis, he says, brings that embedded value to light. By quantifying the fuzzy realm of strategic judgment, where leaps of faith govern decisions, real options analysis fosters the union of finance and strategy. “It’s a way to be a little more precise about intuitive feelings,” says Tom Unterman, CFO of $3 billion Times Mirror Co., the Los Angeles based news and information company. A real options analysis recently bolstered the company’s decision to back away from an acquisition, says Unterman, and wider use of the approach is foreseeable. “We are quite actively looking for ways to apply it,” he says. Casting investment opportunities as real options increased both the top and bottom lines at Cadence Design Systems Inc., a San Jose, California, based provider of electronic design products and services. “We have closed a number of transactions that we would not have closed before,” CEO Ray Bingham declares . . .. The Value of Flexibility Unlike net present value measurements, real options analysis recognizes the flexibility inherent in most capital projects—and the value of that flexibility. To executives familiar with stock options, real options should look familiar. A stock option captures the value of an investor’s opportunity to purchase stock at a later date at a set price. Similarly a real option captures the value of a company’s opportunity to start, expand, constrain, defer, or scrap a capital investment depending on the investment’s prospects. When the outcome of an investment is least certain, real options analysis has the highest value. As time goes by and prospects for an underlying investment become clearer, the value of an option adjusts. Sweep away the rocket science, and real options analysis presents a more realistic view of an uncertain world beset by constant shifts in prices, interest rates, consumer tastes, and technology. To focus strictly on numerical value misses the depth and complexity of real options discipline, observes Nalin Kulatilaka, a professor of finance at Boston University School of Management. Kulatilaka is an evangelist for a methodology that obliges managers to weigh equally all imaginable alternatives, good and bad. Real options analysis liberates managers from notions of accountability that mete out blame when plans don’t go as expected. That’s not a healthy environment for workers or companies that need to be nimble all the time, if not right all the time. “The best decision may lead to a bad outcome,” says Soussan Faiz, manager of global valuation services at oil giant Texaco Inc. “If you are judged on a bad outcome, guess what? People will say,
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‘Why go through that?’” To succeed today, companies must create new options. But unless managers are rewarded for creating them, Faiz warns, “it ain’t gonna happen.” Certainty Is a Narrow Path By taking uncertainty into account, real options analysis fosters a more dynamic view of the world than net present value does. Net present value ultimately boils down to one of two decisions: go or no-go. When the net present value of expected cash flows is positive, companies usually proceed. As a practical consequence managers concentrate on prospects for favorable outcomes. Prospects for unfavorable outcomes get short shrift. In this analysis certainty enjoys a premium—and that’s a narrow path. Even without gaming the numbers to justify projects, this upside bias invites unpleasant surprises. “Unfortunately, discounted cash flow collapses to a single path,” says Texaco’s Faiz. Management and measurement are intertwined, she explains, yet companies manage with an eye to options, but measure performance as if options don’t exist. In the oil business, oil prices don’t remain low for the life of a project; they bounce back. “The likelihood of prices being low for the rest of the project is zero or nearly zero,” says Faiz. But even if prices do remain stagnant, defying the odds, managers don’t snooze the whole time. They wake up and react. Net present value, however, treats investments as if outcomes are cast in stone. This, needless to say, is not realistic. “Net present value makes a lot of heroic assumptions,” warns Tom Copeland, chief corporate finance officer of Monitor, a strategy consultancy in Cambridge, Massachusetts. Typically a multiyear project is plotted along a single trajectory worth pursuing only if the net present value exceeds zero or some hurdle rate. This type of reasoning may satisfy requirements for a midterm exam, says Copeland, but situations in the real world change constantly as new information surfaces. Most managers realize that flexibility ought to be included in valuations, Copeland says. “The bridge they have to cross is understanding the methodology to capture the value of flexibility.” Out of the Ivory Tower Experts have touted the merits of real options for at least a decade, but the sophisticated mathematics required to explain them has penned up those merits in ivory towers. That’s changing, as proponents tout the virtues of real options as a mind-set for decision-making . . . “The kinds of businesses companies go into today are difficult to go into with NPV,” says John Vaughan, vice president for business development
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Box 1.5 (continued)
at M/A-COM, the Lowell, Massachusetts, based wireless products group of AMP Inc . . .. Net present value would have derailed this project long ago, Vaughan insists. “It would have been difficult to sell this business case, because of the high level of uncertainty,” he says. Real options analysis assembles diverse risks in a coherent fashion, Vaughan says, layer upon layer, like a papier-mâché creation. “It very much mimics the venture capitalist approach,” he says, “by timing expenditures to the maturity of the opportunity.” Handle with Care Real options “add richness and perspective I can’t get elsewhere,” says the oil industry executive. But like any metric that relies on judgment, he warns, real options must be used carefully. They are not tamper-proof. “Given enough volatility and time,” he says, “I can make an option a very big number.” Without solid, accurate measures of volatility, real options can lead companies astray. For evaluating an offshore oil lease, look at the history of oil-futures prices; for a petrochemical plant, look at historical futures and options contracts on margins . . . “I don’t think the value of great judgment or intuition is any less in using a more sophisticated model,” Bingham says. To the extent that real options analysis sheds more light on uncertainty, in his view, it provides a critical link between strategy and finance. Says Bingham: “Getting hold of real options will make a CFO more and more relevant and a valuable part of leadership.” In an uncertain world, that’s the sort of vision CFOs rely on. Reprinted with permission from CFO Magazine, website www.cfo.com © CFO Publishing LLC. Publication date: November 1999.
sometimes make wrong decisions with limited adverse consequences. In contrast, when several firms are active in the market, managers are under constant competitive pressure to make the right decisions all the time. Otherwise, the firm might go belly-up. In perfect competition the decisions of a single firm do not have a significant impact on others, and strategic interactions are again inconsequential. In reality, however, industries are rarely either purely monopolistic or perfectly competitive. In the real marketplace that is closer to oligopoly, firms typically respond to their rivals’ actions. This calls for an appropriate methodology, namely game theory.
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Over the last half century game theory has developed into a rigorous framework for assessing strategic alternatives.13 It helps managers formulate the right strategies and make the right decisions under competition. A recent article in CFO magazine epitomizes a renewed interest by companies in using game theory to aid decision-making (see box 1.6). The origins of game theory trace back to the 1900s when mathematicians got interested in studying various interactive games, such as chess and poker. The first comprehensive formulation of the concept of optimal Box 1.6 Game theory in business practice
More Companies Are Using Game Theory to Aid Decision-Making: How Well Does It Work in the Real World? Alan Rappeport, CFO Magazine When Microsoft announced its intention to acquire Yahoo last February, the software giant knew the struggling search firm would not come easily into the fold. But Microsoft had anticipated the eventual minuet of offer and counteroffer five months before its announcement, thanks to the powers of game theory. A mathematical method of analyzing game-playing strategies, game theory is catching on with corporate planners, enabling them to test their moves against the possible responses of their competitors. Its origins trace as far back as The Art of War, the unlikely management best seller penned 2,500 years ago by the Chinese general Sun Tzu. Mathematicians John von Neumann and Oskar Morgenstern adapted the method for economics in the 1940s, and game theory entered the academic mainstream in the 1970s, when economists like Thomas Schelling and Robert Aumann used it to study adverse selection and problems of asymmetric information. (Schelling and Aumann won Nobel Prizes in 2005 for their work.) Game theory can take many forms, but most companies use a simplified version that focuses executives on the mind-set of the competition. “The formal stuff quickly becomes very technical and less useful,” says Louis Thomas, a professor at the Wharton School of Business who teaches game theory. “It’s a matter of peeling it back to its bare essentials.” One popular way to teach the theory hinges on a situation called the “prisoner’s 13. Game theory is concerned with the actions of decision makers conscious that their actions affect those of rivals and that the actions of competitors, in turn, impact their own decisions. When many players can disregard strategic interactions as being inconsequential (perfect competition) or when a firm can reasonably ignore other parties’ actions (monopoly), standard optimization techniques suffice. Under imperfect competition such as in oligopoly, a limited number of firms with conflicting interests interact such that the actions of each can materially influence firm individual profits and values.
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dilemma,” where the fate of two detainees depends on whether each snitches or stays silent about an alleged crime. Many companies are reluctant to talk about the specifics of how they use game theory, or even to admit whether they use it at all. But oil giant Chevron makes no bones about it. “Game theory is our secret strategic weapon,” says Frank Koch, a Chevron decision analyst. Koch has publicly discussed Chevron’s use of game theory to predict how foreign governments and competitors will react when the company embarks on international projects. “It reveals the win-win and gives you the ability to more easily play out where things might lead,” he says. Enter the Matrix Microsoft’s interest in game theory was piqued by the disclosure that IBM was using the method to better understand the motivations of its competitors—including Microsoft—when Linux, the open-source computer operating system, began to catch on. (Consultants note that companies often bone up on game theory when they find out that competitors are already using it.) For its Yahoo bid, Microsoft hired Open Options, a consultancy, to model the merger and plot a possible course for the transaction. Yahoo’s trepidation became clear from the outset. “We knew that they would not be particularly interested in the acquisition,” says Ken Headrick, product and marketing director of Microsoft’s Canadian online division, MSN. And indeed they weren’t; the bid ultimately failed and a subsequent partial acquisition offer was abandoned in June. Open Options wouldn’t disclose specifics of its work for Microsoft, but in client workshops it asks attendees to answer detailed questions about their goals for a project—for example, “Should we enter this market?” “Will we need to eat costs to establish market share?” “Will a price war ensue?” Then assumptions about the motives of other players, such as competitors and government regulators, are ranked and different scenarios developed. The goals of all players are given numerical values and charted on a matrix. The exercise is intended to show that there are more outcomes to a situation than most minds can comprehend, and to get managers thinking about competition and customers differently. “If you have four or five players, with four actions each might or might not take, that could lead to a million outcomes,” comments Tom Mitchell, CEO of Open Options. “And that’s a simple situation.” To simplify complex playing fields, Open Options uses algorithms to model what action a company should take—considering the likely actions of others—to attain its goals. The result replicates the so-called Nash
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Box 1.6 (continued)
equilibrium, first proposed by John Forbes Nash, the Nobel Prize winning mathematician portrayed in the movie A Beautiful Mind. In this optimal state, the theory goes, a player no longer has an incentive to change his position. As a tool, game theory can be useful in many areas of finance, particularly when decisions require both economic and strategic considerations. “CFOs welcome this because it takes into account financial inputs and blends them with nonfinancial inputs,” says Mitchell. Rational to a Fault? Some experts, however, question game theory’s usefulness in the real world. They say the theory is at odds with human nature because it assumes that all participants in a game will behave rationally. But as research in behavioral finance and economics has shown, common psychological biases can easily produce irrational decisions. Similarly John Horn, a consultant at McKinsey, argues that game theory gives people too much credit. “Game theory assumes rationally maximizing competitors who understand everything that you’re doing and what they can do,” says Horn. “That’s not how people actually behave.” (Activist investor Carl Icahn said Yahoo’s board “acted irrationally” in rejecting Microsoft’s bid.) McKinsey’s latest survey on competitive behavior found that companies tend to neglect upcoming moves by competitors, relying passively on sources such as the news and annual reports. And when they learn of new threats, they tend to react in the most obvious way, focusing on near-term metrics such as earnings and market share. Moreover finance executives have their own sets of metrics, and when favored indicators such as net present value clash with game theory models, choices become more complicated. “Sometimes [game theory] tells you things you don’t like,” says Koch. Game theory is still finding its place as a tool for companies, and its ultimate usefulness may depend on how quickly it moves from novelty to accepted practice. Practice in fact may be key. McKinsey takes that to heart with its “war game” scenarios, in which a company’s top managers play the roles of different parties in a simulation. In effect this boils game theory down to the schoolyard lesson that perfection comes through repetition. “Discipline is not a dirty word,” as basketball coach Pat Riley once said. Game theory is one way that companies can assess their options with more discipline. Reprinted with permission from CFO Magazine, website www.cfo.com © CFO Publishing LLC. Publication date: July 15, 2008.
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strategies in a multiple-player setup came with The Theory of Games and Economic Behavior by John von Neumann and Oskar Morgenstern (1944). More critical advancements were made in the early 1950s when John F. Nash Jr. provided a broad mathematical basis for the study of equilibria in strategic conflict situations (Nash 1950a, b, 1951).14 More recently game theory has been applied in many fields, including political science, international relations, military strategy, law, sociology, psychology, and biology. Game theory has revolutionized microeconomics and given a strong analytical basis for studying real market structures. As firm competitiveness involves interactions among many players (firms, suppliers, buyers, etc.), it also brought appealing insights into strategic management. Box 1.7 provides an overview of the basics of game theory by Avinash Dixit. An interview with Professor Dixit concerning the interconnection between real options and game theory and his pedagogical approach is given in box 1.8. To perform strategic analysis, one needs to reduce a complex multiplayer problem into a simpler analytical structure that captures the essence of the conflict situation. As discussed later, conducting such an analysis involves a clear depiction of the “rules of the game,” namely (1) identifying the players, (2) describing their available alternative choices, (3) specifying the information structure of the game, (4) determining the payoff values attached to each possible strategy choice, and (5) specifying the order or sequence of the play.15 From a specified game structure, one may derive useful predictions on how rivals are likely to react (equilibrium strategies) in a given environment. A key question commonly arises: how should managers view these models? Should they interpret them in a literal or in a metaphorical sense? There is no clear-cut answer, but the metaphorical interpretation is generally accepted as more appropriate. One of the underlying premises of standard game theory is the presumed rationality of economic agents.16 This assumption is not always consistent with real-world behavioral phenomena, so excessive mathematical rigor may limit the 14. Before John F. Nash’s work, the focus of game theory was mostly on zero-sum games. Nash’s (1950b) equilibrium concept applies to a large set of problems beyond zero-sum games. Myerson (1999) provides a comprehensive analysis of how the Nash equilibrium concept shaped economic theory. 15. The order of play may affect both the possible actions players can select from and the information they possess at the time they make their decisions. 16. Defining rationality and optimality in a given strategic setting is one of the core objectives of game theory. This has an impact on the choice of the game-theoretic solution concept. Nash equilibrium makes behavioral assumptions beyond common knowledge of rationality.
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Box 1.7 Overview of game theory basics
Avinash K. Dixit, John J. K. Sherrerd ’52 University Professor of Economics, Princeton University Game theory studies interactive decision-making, where the outcome for each participant or “player” depends on the actions of all. If you are a player in such a game, when choosing your course of action or “strategy” you must take into account the choices of others. But in thinking about their choices, you must recognize that they are thinking about yours, and in turn trying to take into account your thinking about their thinking, and so on. It would seem that such thinking about thinking must be so complex and subtle that its successful practice must remain an arcane art. Indeed some aspects such as figuring out the true motives of rivals and recognizing complex patterns do often resist logical analysis. But many aspects of strategy can be studied and systematized into a science—game theory. The Nash Equilibrium The theory constructs a notion of “equilibrium” to which the complex chain of thinking about thinking could converge. Then the strategies of all players would be mutually consistent in the sense that each would be choosing his or her best response to the choices of the others. For such a theory to be useful, the equilibrium it posits should exist. Nash used novel mathematical techniques to prove the existence of equilibrium in a very general class of games. This paved the way for applications. Biologists have even used the notion of Nash equilibrium to formulate the idea of evolutionary stability. Here are a few examples to convey some ideas of game theory and the breadth of its scope. The Prisoner’s Dilemma In Joseph Heller’s novel Catch-22, allied victory in World War II is a forgone conclusion, and Yossarian does not want to be among the last ones to die. His commanding officer points out, “But suppose everyone on our side felt that way?” Yossarian replies, “Then I’d certainly be a dammed fool to feel any other way, wouldn’t I?” Every general reader has heard of the prisoner’s dilemma. The police interrogate two suspects separately, and suggest to each that he or she should fink on the other and turn state’s evidence. “If the other does not fink, then you can cut a good deal for yourself by giving evidence against the other: if the other finks and you hold out, the court will treat you especially harshly. Thus no matter what the other does, it is better for you to fink than not to fink—finking is your uniformly best or ‘dominant’ strategy.” This is the case whether the two are actually guilty, as in some
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episodes of NYPD Blue, or innocent, as in the firm L.A. Confidential. Of course, when both fink, they both fare worse than they would have if both had held out; but that outcome, though jointly desirable for them, collapses in the face of their separate temptations to fink. Yossarian’s dilemma is just a multi-person version of this. His death is not going to make any significant difference to the prospects of victory, and he is personally better off alive than dead. So avoiding death is his dominant strategy. John Nash played an important role in interpreting the first experimental study of the prisoner’s dilemma, which was conducted at the Rand Corporation in 1950. Real-World Dilemmas Once you recognize the general idea, you will see such dilemmas everywhere. Competing stores who undercut each other’s prices when both would have done better if both had kept their prices high are victims of the dilemma. (But in this instance consumers benefit from the lower prices when sellers fink on each other.) The same concept explains why it is difficult to raise voluntary contributions, or to get people to volunteer enough time for worthwhile public causes. How might such dilemmas be resolved? If the relationship of the player is repeated over a long time horizon, then the prospect of future cooperation may keep them from finking; this is the well-known tit-for-tat strategy. A “large” player who suffers disproportionately more from complete finking may act cooperatively even when the small is finking. Thus Saudi Arabia acts as a swing producer in OPEC, cutting its output to keep prices high when others produce more, and the United States bears a disproportionate share of the costs of its military alliances. Finally, if the group as a whole will do better in its external relations if it enjoys internal cooperation, then the process of biological or social selection may generate instincts or social norms that support cooperation and punish cheating. The innate sense of fairness and justice that is observed among human subjects in many laboratory experiments on game theory may have such an origin. Mixing Moves In football, when an offense faces a third down with a yard to go, a run up the middle is the usual or “percentage” play. But an occasional long pass in such a situation is important to keep the defense honest. Similarly a penalty kicker in soccer who kicks exclusively to the goalie’s right, or a server in tennis who goes exclusively to the receiver’s forehand, will fare poorly because the opponent will anticipate and counter the action. In such situations it is essential to mix one’s moves randomly so that on any one occasion the action is unpredictable.
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Box 1.7 (continued)
Mixing is most important in games where the players’ interests are strictly opposed, and this happens most frequently in sports. Indeed recent empirical studies of serving in tennis grand slam finals, and penalty kicks in European soccer leagues, have found the behavior consistent with the theory. Commitments Greater freedom of action seems obviously desirable. But in games of bargaining, that need not be true because freedom to act can simply become freedom to concede to the other’s demands. Committing yourself to a firm final offer leaves the other party the last chance to avoid a mutually disastrous breakdown, and this can get you a better deal. But a mere verbal declaration of firmness may not be credible. Devising actions to make one’s commitment credible is one of the finer acts in the realm of strategic games. Members of a labor union send their leaders into wagebargaining with firm instructions or mandates that tie their hands, thereby making it credible that they will not accept a lower offer. The executive branch of the US government engaged in international negotiations on trade or related matters can credibly take a firm stance by pointing out that the Congress would not ratify anything less. And a child is more likely to get the sweet or toy it wants if it is crying too loudly to hear your reasoned explanations of why it should not have it. Thomas Schelling pioneered the study of credible commitments, and other more complex “strategic moves” like threats and promises. This has found many applications in diplomacy and war, which, as military strategist Karl von Clausewitz told us long ago, are two sides of the same strategic coin. Information and Incentives Suppose that you have just graduated with a major in computer science and have an idea for a totally new “killer app” that will integrate PCs, call phones, and TV sets to create a new medium. The profit potential is immense. You go to venture capitalists for finance to develop and market your idea. How do they know that the potential is as high as you claim it to be? The idea is too new for them to judge it independently. You have no track record, and you might be a complete charlatan who will use the money to live high for a few years and then disappear. One way for them to test your own belief in your idea is to see how much of your own money you are willing to risk in the project. Anyone can talk a good game; if you are willing to put enough of your money where your mouth is, that is a credible signal of your own true valuation of your idea. This is a game where the players have different information; you know the true potential of your idea much better than does your prospective
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financier. In such games, actions that reveal or conceal information play crucial roles. The field of “information economics” has clarified many previously puzzling features of corporate governance and industrial organization, and has proved equally useful in political science, studies of contract and tort law, and even biology. The award of the Nobel Memorial Prize in 2001 to its pioneers, George Akerlof, Michael Spence, and Joseph Stiglitz, testifies to its importance. What has enabled information economics to burgeon in the last twenty years is the parallel development of concepts and techniques in game theory. Aligning Interests, Avoiding Enrons A related application in business economics is the design of incentive schemes. Modern corporations are owned by numerous shareholders who do not personally supervise the operations of the companies. How can they make sure that the workers and managers will make the appropriate efforts to maximize shareholder value? They can hire supervisors to watch over workers, and managers to watch over supervisors. But all such monitoring is imperfect: the time on the job is easily monitored, but the quality of effort is very difficult to observe and judge. And there remains the problem of who will watch over the upper-level management. Hence the importance of compensation schemes that align the interests of the workers and managers with those of the shareholders. Game theory and information economics have given us valuable insights into these issues. Of course, we do not have perfect solutions; for example, we are just discovering how top management can manipulate and distort the performance measures to increase their own compensation while hurting shareholders and workers alike. This is a game where shareholders and the government need to find and use better counterstrategies. From Intuition to Prediction While reading these examples, you probably thought that many of the lessons of game theory are obvious. If you have had some experience of playing similar games, you have probably intuited good strategies for them. What game theory does is to unify and systemize such intuitions. Then the general principles extend the intuitions across many related situations, and the calculation of good strategies for new games is simplified. It is no bad thing if an idea seems obvious when it is properly formulated and explained; on the contrary, a science or theory that takes simple ideas and brings out their full power and scope is all the more valuable for that.
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Box 1.8 Interview with Avinash K. Dixit
1. You have helped establish and popularize both real options and game theory as separate disciplines. How do you see the interconnection or interplay among the two? The real options concept emphasizes the value of flexibility, whereas an irreversible commitment has value in many situations of strategic competition. Analyzing the two together enables us to understand when one prevails over the other, or more generally, the trade-off between the two. This is clearly an important research program. 2. In your teaching of game theory at Princeton you rely a lot on stories, movies, literature, sports, games, and other engaging tools to motivate your students. Can you elaborate on your view or approach? Undergraduate students are rightly skeptical of abstract theory, and demand evidence of its relevance before they will spend their time and effort on studying theory. Game theory is fortunate in having so many compelling and entertaining examples readily available. Using examples to bring out theoretical concepts is similar to the case method used in most business schools. But MBA students are narrowly focused on business and moneymaking. Undergraduates have richer and more varied lives; therefore examples from sports, games, literature, and movies appeal to them. 3. What other current or future areas of research in economic sciences do you find interesting? Connections between economics and other social sciences are enriching them all. Economists are becoming aware of aspects of human behavior
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Box 1.8 (continued)
that differ from selfish rationality assumed in most traditional economic theory; sociologists, political scientists, and even psychologists and anthropologists are learning the value of economists’ conceptual framework of choice and equilibrium and of the issues of endogeneity and identification in empirical work. I find this confluence of the social sciences interesting and exciting. I don’t expect complete reintegration of fields that separated more than a century ago; the benefits of specialization remain. But I do expect much closer communication and collaboration that will benefit research in all these fields.
applicability of game theory in certain real-world situations. A metaphorical interpretation of game-theoretic models can nonetheless hold significant value. Game theory can generally be used to deduce principles and insights from simplified models of reality. Simpler models are generally more prescriptive. Oftentimes, when game theory is applied in more complex situations, it results in no outcome or in multiple equilibria. In such cases complex modeling may lose its predictive ability.17 One objective of microeconomic modeling is to simulate qualitatively the type of environment being studied. Useful insights into an issue or behavior of practical relevance can be then deduced. A simple game-theoretic framework has several advantages for strategic management.18 First, it provides an audit track that enables researchers and practitioners to go back to basic premises. Management should formulate the basic assumptions explicitly and consider whether they are of practical relevance. Second, it offers a methodology that is conducive to rigorous analysis and can help derive novel insights, which might be counterintuitive in some cases. Such insights may hardly stem from a “boxes-and-arrows” conceptual framework.19 Finally, it helps bring discipline by enforcing a common language that enables researchers and managers to compare results and refine earlier models.20 In view of such potential extensions, game theory holds out considerable promise for 17. See Grant (2005, p. 111). 18. This discussion follows Saloner (1991, pp. 120–25, 127). 19. Saloner (1991) includes Porter’s (1980) five-forces framework among these conceptual frameworks for strategic management. A game-theoretic refinement of Porter’s (1980) framework is offered by Brandenburg and Nalebuff (1996). 20. Some microeconomic models are quite tractable for studying new problems. Cournot and Bertrand duopoly models discussed in chapter 3 form the basis for many industrial organization setups which are often much more complex. For instance, the commitment theory addressed in chapter 4 can be understood in light of these two pillar models.
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studying human interactions. This explains the success of game theory particularly in economics and strategic management. The following three fields have greatly benefited from game-theoretic modeling:21 Industrial organization This field focuses on strategic interactions arising in the external environment of the firm, addressing issues such as competitive interactions in oligopolies, first- and second-mover advantages, firm entry and exit decisions, strategic commitment, reputation, signaling and information asymmetries among different players in an industry. •
Organization theory This field focuses on the firm’s internal or organizational aspects such as vertical and horizontal scope and conflicting incentives inside an organization (e.g., optimal compensation schemes).22
•
Interaction between the internal and external environment of the firm This area addresses issues at their interface like optimal incentive schemes in oligopolistic market structures,23 organizational design to achieve competitive advantage, cooperation versus competition in R&D.24 •
A good understanding of the competitive environment will thus enable managers to ascertain the strategic implications of their actions in the marketplace and determine how they should behave. A normative role for the strategic management literature is to provide a broad qualitative understanding of such strategic interactions and give qualitative prescriptions for managerial action.25 Although game theory has witnessed rapid developments since the 1950s, its use for practical strategic management purposes has remained limited so far.26 Nevertheless, some managers already incorporate game-theoretic thinking into their planning. 21. This discussion follows Saloner (1991, pp. 119–20). 22. Theories of the firm attempt to explain why firms exist and operate as they do. They include property-rights theory, incentive-system theory, rent-seeking theory, adaptation theory, and contract theory. 23. Ferschtman and Judd (1987), for example, argue that in case of product market competition, a firm has an incentive to design incentive schemes in a way that would not be optimal for a stand-alone organization. In their model, managers have an incentive to maximize output in a quantity competition setting, which makes the firm better off since the rival interprets the incentive contract as a commitment. This exemplifies the possible use of industrial organization thinking to better the understanding of firms’ organizational design. 24. D’Apremont and Jacquemin (1988) analyze the effect of R&D spillovers on the incentive to cooperate or compete during the R&D stage and the market competition stage. 25. See Saloner (1991, pp. 107–31). 26. In politics, game theory has been used to analyze the 1962 Cuban missile crisis, President Reagan’s 1982 tax cut, and certain public auctions.
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1.4 An Integrative Approach to Strategy Both corporate finance and game theory provide useful insights for strategic management. As discussed by Jean Tirole in box 1.9, the interface between finance and game theory enables attaining a better understanding on a number of firm- or market-related issues. Viewed separately, however, these approaches have limited applicability. Integrating these approaches in a consistent manner is at the core of the option games approach. Standard real options analysis overcomes many of the drawbacks of the NPV approach but neglects other aspects. When management assesses its real options, it must determine whether the benefits resulting from the exercise of its options are fully appropriable. Kester (1984) distinguishes two categories of real options depending on whether the benefits are proprietary or shared. If management has an exclusive exercise right, retaining all potential benefits for itself, the investment opportunity is a proprietary option.27 When the firm is not in a position to appropriate all of the project’s benefits for itself but rivals share the same opportunity, it is a shared option.28 In this case, the presence of market contenders introduces strategic externalities (positive or negative) that can significantly affect the value and optimal exercise strategy of the firms’ real options. The value loss resulting from strategic interactions is seen as a competitive value erosion. Standard or naïve real options analysis typically assumes a proprietary or monopolistic mind-set, ignoring such shared options.29 A firm here formulates its investment decisions in isolation, disregarding interactive competition. 27. Rivals’ investment decisions have no material impact on project values or optimal strategies. For instance, a monopolist protected by significant entry barriers faces such a situation. Proprietary options are also encountered when a firm is granted an infinitely lived patent on a product that has no close substitutes or when it has unique know-how of a technological process. 28. Shared options include the opportunity to launch a new product that is unprotected from the entry of close substitutes, or the opportunity to penetrate a newly deregulated market. 29. One reason why strategic interactions among option holders are not typically considered in standard real options analysis is that early on real options were seen as an extension of standard option-pricing theory to real investment situations. In capital markets, except in special cases like valuation of warrants, strategic interactions among option holders rarely affect the asset or the option values. Certain continuous-time real option models attempt to account for market and competitive uncertainty in an exogenous manner, such as through a higher dividend yield or a jump process. These models represent an improvement over standard models developed with a monopolistic mind-set, but fall short of adequately accounting for the endogenous nature of strategic interactions in an oligopolistic setting. Trigeorgis (1991) discusses continuous-time real options models involving strategic uncertainty exogenously.
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Box 1.9 Interview with Jean Tirole
1. You have contributed greatly to extending game theory for the analysis of economic problems. What are the merits of this mathematical discipline for economic analysis? Which other social sciences do you believe can benefit from the use of game theory? Game theory aims at describing and predicting behaviors in environments in which actors are interdependent and have potentially conflicting objectives. It deepens our understanding of when the quest for specific goals may lead to inefficiencies and of how players choose actions with an eye on changing other actors’ incentives. As such, game theory applies to all social sciences and beyond. The most obvious applications, besides economics, include political sciences, sociology, law, and psychology. Psychology might look like an outlier as it usually focuses on the individual, but it is not. Experimental evidence confirms the old notion that we “play games with ourselves.” These can be represented as games among successive incarnations of the self. Biologists use game theory to understand mutualism between species, inefficient signals, or fights. Computer scientists also take a keen interest in game theory. Part of the appeal of game theory is that it accommodates diverse objective functions, which enables us to conceptualize the behavior of different actors, from consumers to politicians, from firms to suborganisms. Game theory more and more integrates behavioral approaches. Mainstream game theory focuses on optimal strategies given the strategic interdependences and the actors’ limited information and various constraints. This rational choice approach has served social sciences well by identifying the key strategic features of conflict situations. At the same time, limited cognition and various
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Box 1.9 (continued)
behavioral biases are being increasingly incorporated into our thinking about strategic interactions, extending the reach of game theory beyond purely rational choice. Finally, experimental economists have been testing our equilibrium concepts and behavioral predictions, and empiricists use game theory to put more structure on their estimation strategies. 2. Your work on industrial organization has helped popularize this important discipline and extend its areas of application. Do you think industrial organization will become increasingly more useful for managerial practice and understanding or predicting of market developments? Yes. Game theory and its applications to industrial organization have made their way into business books and have affected managerial practice. For example, concepts developed in industrial organization are used in deliberations on how to design new platforms and get all sides on board. Game theory is taught to MBAs and strategy textbooks now incorporate game-theoretic thinking. Game-theoretic analyses have become a language for antitrust practitioners to conceptualize impacts of behaviors on market outcomes. Empirical work on estimating demand and strategic choices of price and non–price competition also make substantial use of game-theoretic industrial organization. 3. Do you see a connection between industrial organization and corporate finance? In what ways? Can game theory help reshape corporate finance as it has reshaped standard microeconomics? There is indeed a strong connection between industrial organization and finance. I am really happy that your book “cross-breeds” options theory and industrial organization and connects it to business, as this is an area of very fruitful cross-fertilization. Not only do finance and industrial organization share common tools (e.g., economics of incentives, game theory), they also interface in many areas. For example, it is hard to fully understand predation or entry into industries without understanding financial constraints and therefore corporate finance. Conversely, the industrial organization of finance and banking is a hot topic on our research agenda. Game theory has already made its way into various subfields of corporate finance: takeover strategies, liquidity hoarding, expectations of refinancing and bailouts, bank runs, issues of securities under asymmetric information, and conglomerate strategies are just a few examples. Many areas of corporate finance have benefited significantly from importing ideas coming from game theory.
37
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Table 1.2 Comparison of main advantages and drawbacks of standard stand-alone approaches Approach
Advantages
Drawbacks
Standard NPV
Easy to use; convincing logic; widely used; easy to communicate
Assumes precommitment to a given plan of action, often treating investment as a one-time decision (“invest now or never”); ignores flexibility to adapt to unexpected market developments or strategic interactions
Real options
Incorporates market uncertainty and managerial flexibility; recognizes that investment decisions can be delayed, staged, or adjusted under certain future contingencies
Typically applied to the valuation of a monopolist or proprietary option; ignores (endogenous) competitive interactions
Game theory
Incorporates competitive reactions endogenously; considers different player payoffs
Typically disregards market uncertainty involving stochastic variables
Game theory has been generally applied to studying strategic interactions in settings involving steady or deterministically changing states, where players could accurately predict the evolution of the external environment. Standard game theory falls short of explaining the firm’s incentive to stay flexible to react to unexpected developments. Under uncertainty this prescription of standard game theory is inadequate.30 The main advantages and drawbacks of each stand-alone approach (standard NPV, standard real options analysis, and game theory) are summarized in table 1.2. Many real-world problems, however, require a simultaneous assessment of both market (exogenous) as well as competitive (endogenous) uncertainty. Stand-alone NPV, real options analysis, and game theory alone fail in providing the necessary tool kit. The NPV paradigm deals with static situations where firms make now-or-never decisions or precommit to a certain plan of action. Real options analysis allows for 30. Different forms of uncertainty are considered in game theory. For instance, some solution concepts (e.g., Bayesian and perfect Bayesian equilibrium) are designed to address problems involving information uncertainty. This form of uncertainty is not equivalent to what we consider here. In stochastic environments, payoffs may be affected by exogenous factors or shocks, whose future values are not known with certainty but follow a known probability law. The appendix of the book provides a discussion of such stochastic processes.
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Table 1.3 Classification of decision situations and relevant theories Decision theory (no strategic interaction)
Game theory (strategic interaction)
Net present value (DCF) Chapter 1
Static industrial organization Chapter 3
Deterministic
Resource extraction/ forest economics Chapter 9
Dynamic industrial organization Chapters 4, 11, 12
Stochastic
Real options analysis Chapters 5, 9, appendix
Option games Chapter 6 onward
Static
Dynamic
dynamic decision-making in situations where firms face exogenous stochastic uncertainty.31 Static industrial organization (IO) has limited applicability to situations involving simultaneous games where firms are ignorant of both past and future actions and payoffs.32 Dynamic industrial organization analysis permits a long-term perspective but assumes steady state or a deterministic evolution of the market environment.33 We here discuss an integrated approach employed to help overcome the shortcomings of stand-alone approaches, analyzing key value drivers concurrently. Option games are meant to capture dynamic strategic interactions in stochastic environments. Table 1.3 positions the option games approach within the traditional decision and game-theory paradigms.34 We indicate in which chapters each approach is most relevant in the book. The importance of incorporating options analysis and game theory is confirmed by the number of Nobel Prizes awarded in related fields. A 31. Real options analysis provides many applications for dynamic programming. The term “dynamic programming” was originally used by Bellman (1957) to describe a recursive process for solving dynamic problems. This method can be extended to stochastic environments. Stochastic dynamic programming or stochastic control is discussed by Harrison (1985), Dixit (1993), and Stokey (2008), with applications in economics and finance. 32. Static industrial organization rests on static game theory with the related notion of, for example, Nash or Bayesian Nash equilibria. See Osborne (2004) for an introduction. 33. Fudenberg and Tirole (1986) present a number of dynamic models of oligopoly. This field rests on dynamic game theory. It involves the use of “dynamic” solution concepts such as subgame perfect Nash, perfect Bayesian, or sequential equilibrium. A subfield is differential games that study dynamic strategic interactions in settings where an industry state evolves according to a differential equation. See Dockner et al. (2000) for an introduction to differential games. 34. Option games share some aspects with stochastic timing and differential games.
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Table 1.4 Selected Nobel Prizes awarded in Economic Sciences Year
Nobel Prize winner(s)
Noted contribution
2007
L. Hurwicz, E. S. Maskin, R. B. Myerson
For laying the foundations of mechanism design and contract theory
2005
R. J. Aumann, T. C. Schelling
For enhancing our understanding of conflict and cooperation through game theory analysis
2001
G. A. Akerlof, A. M. Spence, J. E. Stiglitz
For analyzing markets with asymmetric information
1997
R. C. Merton, M. S. Scholes
For developing the option pricing method to value derivatives (and thereby real options)
1994
J. C. Harsanyi, J. F. Nash Jr., R. Selten
For their analysis of equilibria in the theory of noncooperative games
1990
H. M. Markowitz, M. H. Miller, W. F. Sharpe
For their pioneering work in the theory of financial economics
1982
G. J. Stigler
For his studies of industrial structures, functioning of markets, and causes and effects of public regulation
1981
J. Tobin
For his analysis of financial markets, expenditure decisions, employment, production, and prices
1978
H. A. Simon
For his research into the decisionmaking process within economic organizations
1975
L. V. Kantorovich, T. C. Koopmans
For their contributions to the theory of optimum allocation of resources
1970
P. A. Samuelson
For developing dynamic economic theory and raising the rigor of analysis in economics
Source: Nobel Prize committee website.
selected list of Nobel Prize awards in economic sciences is shown in table 1.4. Real options analysis is a natural extension of major breakthrough developments in financial economics to real investments. It builds on the seminal works of Paul Samuelson, Robert C. Merton, Fischer Black, and Myron Scholes. Concurrently the analysis of industrial organization has been greatly facilitated by developments in game theory. Von Neumann, Morgenstern, and Nash have made significant early contributions to game theory. Selten, Harsanyi, Schelling, and Aumann also earned Nobel
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Prizes for refinements to the theory.35 Option games, being at the intersection of option and game theories, benefited from the cumulative developments in these subfields of economic sciences. Today option games represent a powerful strategic management tool that can guide practical managerial decisions in a competitive context, as discussed by Ferreira, Kar, and Trigeorgis (2009). It enables a more complete quantification of market opportunities while assessing the sensitivity of strategic decisions to exogenous variables (e.g., demand volatility, costs) and competitive interactions. 1.5
Overview and Organization of the Book
The book is organized in three parts. Part I, “Strategy, Games, and Options,” presents the three building blocks or prerequisite fields for the option games approach. Chapter 2, “Strategic Management and Competitive Advantage,” reviews the main strategic management paradigms used to analyze or explain a firm’s performance in creating value for shareholders. We describe industry and competitive analysis and discuss how to create sustainable competitive advantage in an industry utilizing generic competitive strategies. Chapters 3 and 4 on “Market Structure Games” introduce game theory principles and industrial organization concepts providing economic foundations for strategic management. Chapter 3, focusing on “Static Approaches,” discusses benchmark cases where firms interact in one-time situations. Quantity and price competition are discussed in detail. Chapter 4, focusing on “Dynamic Approaches,” supplements the previous analysis by allowing firms to interact in the marketplace over many periods, attempting in the long term to shape the market in their own advantage or collaborating with rivals for mutual benefit. Chapter 5 on “Uncertainty, Flexibility, and Real Options” discusses the strategyformulation challenges facing the firm when the underlying market is uncertain. Motivated by various sources of uncertainty electricity utilities face today, we discuss how real options analysis can be used to analyze such situations, value strategic options, and optimally chose among them. We also briefly discuss discrete-time and continuous-time tools for the pricing of embedded real options. We ignore here the fact 35. Akerlof, Spence, Stiglitz, Hurwicz, Maskin, and Myerson won the Nobel Prize for insightful applications of game theory for the understanding of incentives in social groups (e.g., industrial organization, contract theory).
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that many firms may face counteracting business opportunities affected by rival behavior. Part II, “Option Games: Discrete-Time Analysis,” fleshes out in more detail in discrete time the integration of real options with game theory and industrial organization and explains how to capture the flexibility and strategic-interaction aspects of real investment situations. Chapter 6 presents core issues in option games analysis, namely optimal investment timing under uncertainty and competition and the trade-off between commitment and flexibility. We provide a number of examples to illustrate the discrete-time option games approach. Chapter 7, “Option to Invest,” rigorously sets the premise for analyzing option games in discrete time, building upon models developed in chapter 3. Chapter 8, “Innovation Investment in Two-Stage Games,” discusses at length the trade-off between commitment and flexibility in sequential investment settings. The focus, here, is on two-stage competition models where real options analysis tools are combined with industry organization insights. The two-stage analysis provides guidance into how and when strategic investments enhance value creation or are detrimental to the firm. We examine appropriate investment strategy applications in different settings, such as R&D and advertising. Part III, entitled “Option Games: Continuous-Time Models,” extends the analysis of option games by use of continuous-time modeling techniques. Chapter 9, “Investment and Expansion Option: Monopoly,” introduces the methodology employed throughout part III and sets the benchmark case of a monopolist firm. Two categories of options are discussed, the option to invest in a new market and the option to expand an existing market. Chapter 10, “Oligopoly: Simultaneous Investment,” extends the analysis to simultaneous investment oligopoly markets. Chapter 11, “Leadership and Early-Mover Advantage,” discusses the appeal of having a competitive advantage to turn the investment-timing game into one’s own advantage. Chapter 12, “Preemption versus Cooperation in a Duopoly,” deals with preemptive investments and the possibility of tacit collusion among firms, delaying investment until a later date. Chapter 13, “Extensions and Other Applications,” provides a short overview of important contributions on various subjects and other applications discussed in the literature. The appendix that follows discusses the basics of stochastic processes and provides a compendium of tools in stochastic calculus and control for the more analytically minded reader. To smooth out the exposition in the text, part III occasionally refers to this appendix when a derivation is more involved.
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Conclusion In this introductory chapter we discussed key changes firms have faced over the past decades. We discussed the development and changes in a challenging business environment and the evolution of strategy, and examined to which extent the two pillar approaches underlying this book, namely game theory and real options analysis, can be rigorous and relevant for analyzing and understanding business strategies. We concluded with the need to combine both into an integrative option games approach. Selected References Grant (2005) discusses the role of strategy in corporate performance, stressing the need for sound strategy formulation based on consistent principles. Myers (1984) discusses the gap between financial theory and its practical implementation in corporate strategy, highlighting the differential insights corporate finance can add to strategy. Saloner (1991) and Rasmussen (2005) discuss the usefulness of game theory for strategic management, emphasizing its prescriptive value in competitive settings involving strategic interactions. Grant, Robert M. 2005. Contemporary Strategy Analysis, 5th ed. Malden, MA: Blackwell. Myers, Stewart C. 1984. Finance theory and financial strategy. Interfaces 14 (1): 126–37. Rasmussen, Eric. 2005. Games and Information: An Introduction to Game Theory. Oxford: Blackwell. Saloner, Garth. 1991. Modeling, game theory, and strategic management. Strategic Management Journal 12 (Winter): 119–36.
I
STRATEGY, GAMES, AND OPTIONS
2
Strategic Management and Competitive Advantage
Managing an enterprise in an uncertain competitive environment is not an easy task. Strategic management attempts to explain why some firms are more successful than others in the marketplace. At the core of strategy is a dilemma between flexibility and commitment. Flexibility to adapt strategy and operations is clearly valuable when the environment changes unexpectedly. An early investment commitment may yet have strategic value because it can influence the behavior of rivals in equilibrium, potentially creating a future competitive advantage for the firm. The flexibility perspective partly draws on the resource-based view of the firm and core-competence arguments: a firm should invest in resources and competencies that give it a distinctive ability to pursue a set of market opportunities. During the 1990s this view became a dominant paradigm in strategic management. It has also helped spark further developments in what is referred to as the “knowledge-based” view of the firm. The commitment view has been firmly anchored in industrial organization. In recent years competitive advantage has become rather temporary because industries and competition within industries change continually. Firms make tremendous efforts to sustain competitive advantage and protect their future growth options from duplication efforts by rivals. Whether competitive advantage can be sustained in the long term partly depends on mechanisms put in place by the firm to renew and protect its resource position and capabilities. In section 2.1 we provide an overview of some of the prevailing strategic management concepts and theories. We discuss in section 2.2 several approaches used in practice that help provide a deeper understanding of a given industry. We then examine, in section 2.3, the sources of competitive advantage and their sustainability as an industry evolves.
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2.1
Chapter 2
Strategic Management Paradigms
Various paradigms in strategic management approach the underlying sources of firm value creation from distinct viewpoints. Categorized in figure 2.1 are the main strategic management approaches. Many of these managerial perspectives trace their roots to primary economic disciplines (microeconomics, organization theory, finance). Behind many of them lies the notion that competitive advantage is a function of the firm’s position, resources, and capabilities compared to those of its rivals, the opportunities these resources and capabilities create in a dynamic environment, and the firms’ adaptive capability to respond to market changes. It is commonplace to distinguish between approaches with an external perspective from those with an internal firm view. The former consider competitive advantage and value creation through the eyes of an external observer, focusing on external forces, opportunities, and threats in the external environment. The latter focus on internal forces within the firm, viewing value creation from in-house combinations of expertise, resources, and capabilities. These perspectives consider competitive advantage as dependent on unique, firm-specific resources and capabilities that enable the firm to create and exploit advantaged opportunities. Real options and option games lie at their intersection and can bring together these complementary perspectives. Real options are created by internal adaptive capabilities developed over time but may be affected by the actions of external parties. 2.1.1
External View of the Firm
Outside forces, such as firm’s rivals, suppliers, and customers, continually shape the external environment. External approaches view the sources of value creation as lying in market imperfections, synergies, or economies of scale. The industry and competitive analysis framework popularized by Michael Porter (1980) views strategy in terms of industry structure, entry deterrence, and strategic positioning. Key competitive variables include entry and exit barriers, as well as product differentiation and availability of information. Porter proposes to look at rivalry situations among suppliers of a product and analyze the impact of additional forces, such as the threat from substitute products and technology disruption, new market entry, and the bargaining power of suppliers and buyers. He emphasizes actions the firm should take to protect itself from these market forces and threats. Another external approach is that of strategic
Rumelt (1984) Wernerfelt (1984) Collins and Montgomery (1995)
Schelling (1960) Shapiro (1989) Ghemawat (1991) Dixit and Nalebuff (1991)
Dixit and Pindyck (1994) Trigeorgis (1996)
Real options
Dynamic capabilities/ core competencies
Prahalad and Hamel (1990) Teece, Pisano, and Shuen (1997)
Transaction costs
Property rights
Organization theory
Internal view of the firm
Resourcebased view
Agency theory
Strategic conflict
Game theory/ industrial organization
External view of the firm
Porter (1980)
Industry and competitive analysis
Structure conduct performance
Microeconomic theory
Grenadier (2000) Huisman (2001) Smit and Trigeorgis (2004)
Option games
Option pricing
Financial economics
Figure 2.1 Strategic management frameworks Rows 2 and 3 are related: economic theory (row 2) supports notions developed in strategic management (row 3) via formal models.
Representative authors
Strategic management
Economic theory
Broad field
Strategic Management and Competitive Advantage 49
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conflict (e.g., Shapiro 1989), which focuses on conflict behavior and views value creation as the result of strategic moves in a competitive environment. Game theory ideas are used to help management understand better the strategic interactions among rivals, predict rivals’ reactions, and determine the optimal competitive strategy. 2.1.2
Internal View of the Firm
The internal view of the firm traces its roots to The Theory of the Growth of the Firm by Edith Penrose (1959). This approach became known subsequently as the resource-based view of the firm in articles by Wernerfelt (1984), Rumelt (1984), Teece (1984), and others.1 Its distinguishing characteristic is that competitive advantage arises from within the firm. A firm becomes profitable not so much because it undertakes strategic investments that may deter entry or raise prices above long-run cost, but rather because it manages to achieve significantly lower costs or obtain markedly higher quality or product performance through internal mechanisms. Excess economic profits stem from imperfect markets for firmspecific intangible assets like distinctive competences, know-how, and capabilities. A more recent variant rests on corporate capabilities to adapt in environments of rapid technological change. The theory of “dynamic capabilities” proposed by Teece, Pisano, and Shuen (1997) views capabilities to adapt in a changing environment as resting on distinctive processes, shaped by the firm’s asset position and the evolutionary path it has adopted or inherited. Box 2.1 discusses the concept and process of strategy and their evolution in a changing and rapidly transformed competitive landscape. 2.2
Industry and Competitive Analysis
A key objective of strategic management is to help decision makers understand what leads to a firm’s success. Strategic management provides both explanation and direction. A useful strategy framework should address the following types of questions: Why are some firms more successful than others? Are there different paths to success? Can some firms be successful by adopting product innovation, and others by enhancing operational efficiency via process innovation? 1. According to Wernerfelt (1984), the resource-based view aims to understand the relationship between profitability and the different ways of managing resources over time. Resources are “those tangible and intangible assets which are tied semi-permanently to the firm” (p. 172).
Strategic Management and Competitive Advantage
Box 2.1 Strategy in a changing competitive environment
Changes in the Competitive Battlefield C. K. Prahalad, Financial Times Many of the concepts used in strategy were developed during the late 1970s and 1980s when underlying competitive conditions evolved within a well-understood model . . .. While the canvas available to today’s strategists is large and new, companies will need to understand global forces, react quickly, and innovate when defining their business models . . .. It is hardly surprising that the conceptual models and administrative processes used by managers often outlast their usefulness. It takes researchers time, after all, to identify new problems and emerging solutions before they can produce theories about them. Then there is the time lag between the development of these theories and their conversion into common business practice. Where management concepts are concerned, this time lag—often a decade—brings with it an interesting conundrum. In an era of rapid and disruptive change in the economic, political, social, regulatory, and technological environment do managers have to discard established and tested analytical tools equally as fast? How can they identify the ongoing relevance of concepts and tools in a changing environment? The Heritage of Strategy Concepts The most prevalent and widely used tools of strategy analysis are: strength weakness, opportunities, and threats (SWOT) analysis, industry structure analysis (five forces), value chain analysis, generic strategies, strategic group analysis, barriers to entry, and others of the genre . . .. The concepts and tools—many of them the staples of economists—were adopted and simplified for the use of managers. The formalization of these concepts was instrumental in pushing strategy development from the realm of “the intuitive genius of the founder or a top manager” to that of logical process. However, most of these concepts were developed during the late 1970s and the decade of the 1980s. During this period, underlying competitive conditions evolved but within a well-understood paradigm. A major competitive disruption during this period, certainly for US and European companies, was the spectacular success of Japanese manufacturing in industries as diverse as steel, consumer electronics, autos, and semiconductors. The sources of competitive advantage, during this decade, accrued to those who could wrest major efficiencies in operations through focus on quality, cycle time, reengineering, and team work. Operational efficiencies, within a relatively stable industry structure paradigm, became the focus. In fact this focus on wresting competitive advantage through operational efficiencies led some managers to believe that strategy was unimportant and management was all about implementation.
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Box 2.1 (continued)
The Emerging Competitive Landscape The decade of the 1990s has witnessed significant and discontinuous change in the competitive environment and an accelerating global trend to deregulate and privatize. Large and key industries like telecoms, power, water, health care, and financial services are being deregulated. Countries as diverse as India, Russia, Brazil, and China are at various stages of privatizing their public sectors. Technological convergence—such as that between chemical and electronic companies; computing, communications, components, and consumer electronics; food and pharmaceuticals; and cosmetics and pharmaceuticals—is disrupting traditional industry structures . . .. Ecological sensitivities and the emergence of nongovernmental organizations such as the green movement are also new dimensions of the competitive landscape. Are these discontinuities changing the very nature of the industry structure—the relationships between consumers, competitors, collaborators, and investors? Are they challenging the established competitive positions of incumbents and allowing new types of competitors and new bases for competition to emerge? We can identify a long list of discontinuities and examples to illustrate each one of them . . .. We need to acknowledge the signals (weak as they may be) of the emergence of a new competitive landscape where the rules of engagement may not be the same as they were during the decade of the 1980s. Strategists have to make the transition from asking the question: How do I position my company and gain advantage in a known game (a known industry structure)? Increasingly the relevant question is: How do I divine the contours of an evolving and changing industry structure and therefore the rules of engagement in a new and evolving game? Industries represent such a diversity of new, emerging, and evolving games. The rules of engagement are written as companies and managers experiment and adjust their approaches to competition. Strategy in a Discontinuous Competitive Landscape Strategists must start with a new mind-set. Traditional strategic planning processes emphasized resource allocation—which plants, what locations, what products, and sometimes what businesses—within an implicit business model. Disruptive changes challenge the business models. Four transformations will influence the business models and the work of strategists in the decades ahead: The strategic space available to companies will expand Consider, for example, the highly regulated power industry. All utilities once looked alike and their scope of operations was constrained by public utility commissions and government regulators. Because of deregulation, utilities can now determine their own strategic space. Today utilities have
•
Strategic Management and Competitive Advantage
Box 2.1 (continued)
a choice regarding the level of vertical integration: Do I need to be in power generation? Do I need to be in power transmission? Companies can unbundle assets and can also segment their businesses: Should we focus more on industrial or domestic consumers? They can decide their geographical scope: Should I become global, regional, national, or just remain local? And finally, they can change their business portfolio: Should I invest in water, telecoms, gas lines, services? The forces of change—deregulation, the emergence of large developing countries such as India, China, and Brazil as major business opportunities—provide a new playing field. Simultaneously forces of digitalization, the emergence of the Internet, and the convergence of technologies provide untold new opportunities for strategists. The canvas available to the strategist is large and new. One can paint the picture one wants. Business will be global Increasingly the distinction between local and global business will be narrowed. All businesses will have to be locally responsive and all businesses will be subject to the influences and standards of global players. Consider, for example, McDonald’s and Coca-Cola—held up as examples of truly global players unconstrained by local customers and national differences. In India, McDonald’s had to change its recipe to serve lamb (instead of beef) and vegetarian patties (a radical departure from its normal western fare). Coke had to recognize the power of “Thums Up,” a local cola (which Coke purchased) and promote that product. The need for local responsiveness, especially when global companies want to penetrate markets with different levels of consumer purchasing power, is very clear. On the other hand, Nirula’s, a local fast-food chain in India, was, in its own restaurants, forced to respond to the cleanliness and ambience of McDonald’s. This is a case of global standards being imposed on a local player. Global and local distinctions will remain in products and services. Globalization may have as much to do with standards—quality, service levels, safety, environmental concerns, protection of intellectual property, and talent management. Needless to say, globalization will force strategists to come to terms with multiple geographical locations, new standards, capacity for adaptation to local needs, multiple cultures, and collaboration across national and regional boundaries in everything from manufacturing, product development, global account management, and logistics.
•
Speed will be a critical element Given the nature of competitive changes, speed of reaction will be a critical element of strategy. At a minimum it will challenge the yearly planning cycle. For example, consider the traditional strategic planning process in a
•
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Box 2.1 (continued)
large company. The process of strategy discussion and commitments typically starts in October. It identifies the strategic issues for the next calendar year and three to four years hence. What is the use of such a process in an Internet-based start up? Speed of reaction, not tied to a rigid corporate calendar is of the essence. Strategy must be a topic of discussion and debate all the time not just during the planning sessions—strategy making and thinking cannot be a “corporate rain dance” during October! Speed is also an element in how fast a company learns new technologies and integrates them with the old. As all traditional companies are confronted with disruptive changes, the capacity to learn and act fast is increasingly a major source of competitive advantage. Innovation is the new source of competitive advantage Innovation was always a source of competitive advantage. However, the concept of innovation was tied to product and process innovations. In many large companies, the innovation process is still called the “product creation process.” Reducing cycle time, increasing modularity, tracking sales from new products introduced during the last two years as a percentage of total sales, and global product launches were the hallmarks of an innovative company. Increasingly the focus of innovation has to shift toward innovation in business models. For example, how does an auctionbased pricing market (e.g., airlines, hotels) in an industry with excess capacity change the business model? How do you think about resources available to the company for product development when customers become co-developers of the product or service? Should we have an expanded notion of resource availability? What impact does mass customization or, more importantly, personalization of products and services have on the total logistics chain? Business innovations are crucial in a competitive landscape subject to disruptive changes.
•
Strategy in the Next Millennium Given the dramatic changes taking place in the competitive landscape, I believe that both the concept of strategy and the process of strategy making will change. Older approaches will not suffice. Managers will have to start with two clear premises. First, they can influence the competitive environment. Strategy is not about positioning the company in a given industry space but increasingly one of influencing, shaping and creating it. What managers do matters in how industries evolve. This is not just about the reactions of large, well-endowed companies. Smaller companies can also have an impact on industry evolution . . .. Second, it is not possible to influence the evolving industry environment if one does not start with a point of view about how the world can be, not how to improve what is available but how radically to alter it. Imagining a new competitive space
Strategic Management and Competitive Advantage
Box 2.1 (continued)
and acting to influence the migration toward that future is critical. Strategy is therefore not an extrapolation of the current situation but an exercise in “imagining and then folding the future in.” This process needs a different starting point. This is about providing a strategic direction—a point of view—and identifying, at best, the major milestones on the way. There is no attempt to be precise on product plans, or budgets. Knowing the broad contours of the future is not as difficult as people normally assume. For example, we know with great uncertainty the demographic composition of every country. We can recognize the trends—the desire for mobility, access to information, the spread of the web, and the increasing dependence of all countries on global trade. The problem is not information about the future but insights about how these trends will transform industries and what new opportunities will emerge. While a broad strategic direction (or strategic intent and strategic architecture) is critical to the process, it is equally important to recognize that dramatic changes in the environment suggest managers must act and be tactical about navigating their way around new obstacles and unforeseen circumstances. Tactical changes are difficult if there is no overarching point of view. The need constantly to adjust resource configuration as competitive conditions change is becoming recognized. A critical part of being strategic is the ability quickly to adjust and adapt within a given strategic direction. This may be described as “inventing new games within a sandbox,” the sandbox being the broad strategic direction. The most dramatic change in the process of strategy making is the breakdown in the traditional strategy hierarchy—top managers develop strategy and middle managers implement it. By its very nature discontinuous change in the competitive environment is creating a whole new dynamic. People who are close to the new technologies, competitors, and customers appear as managers in the middle. They have the information, urgency, and motivation to act. They are also the ones who have direct control over people and physical resources. Top managers, in an era of discontinuous change, are rather removed from the new and emerging competitive reality. . . . Middle managers must take more responsibility for developing a strategic direction and, more important, in making decentralized decisions consistent with the broad direction of the company. The involvement of middle managers is a critical element of the strategy process. Finally, creating the future is a task that involves more than the traditional stand-alone company. Managers have to make alliances and collaborate with suppliers, partners, and often competitors to develop new standards, infrastructure, or new operating systems. Alliances and networks are an integral part of the total process. This requirement is so well understood that it is hardly worth elaborating here. Resources available
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Box 2.1 (continued)
to the company are dramatically enhanced through alliances and networks. The New View of Strategy The emerging view of strategy contrasts dramatically with the traditional view . . .. The shift in emphasis in the concept of strategy and the process of strategy making is dramatic. It is clear the disruptive forces that have wrought this change are accelerating. It is time for managers to abandon the comfort of the traditional, and tried and tested, tools and concepts and embrace the new. Disruptive competitive changes will challenge the status quo. Those who take up the challenge and proactively change will create the future. The markets will decide the drivers, passengers, and the rate of “roadkill” soon enough. Reprinted from Financial Times. Publication date: October 4, 1999.
Industry and market attractiveness
Revenue position compared to rivals
Cost position compared to rivals
Firm profitability Firm’s relative competitive positioning in the industry
Figure 2.2 Drivers of firm profitability From Besanko et al. (2004, p. 360)
Figure 2.2 summarizes the drivers of firm profitability according to Besanko et al. (2004). The first driver determines whether a firm operates in an attractive market or industry, as can be ascertained via detailed industry analysis. Profitability does not only vary across industries but also across firms within a given industry, requiring a closer look at the revenue or cost position of the firm relative to its competitors. Analyzing the firm’s environment involves taking a close look at a range of factors, some of which may be beyond the firm’s control (e.g., macroeconomic factors) as well as at industry-specific factors. We examine below how to analyze the macroeconomic environment and subsequently examine industry-specific factors.
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Macroeconomic Analysis
The firm’s macroeconomic environment can have a critical impact on industry attractiveness. One way suggested to analyze the external environment is the PEST framework (standing for politics, economics, social culture, and technology). Politics (e.g., environmental legislation, antitrust rulings, tax policies, employment laws, trade restrictions, tariffs) may influence the profitability and viability of the overall industry, as well as specific firms within it. For multinational corporations (MNCs), assessing the impact of such political factors can be involved as MNCs have to constantly monitor governmental stability, monitor import and export regulations, and be abreast of local business legislations.
•
Economics can have a serious impact on the profitability of an industry or firm. The unemployment level, the relative value of the domestic versus foreign currencies, inflation, growth, and productivity can influence the health of the economy, by way of consumer demand or confidence. These economic factors determine the growth potential. A poor economic situation, such as the recent financial crisis, may lead to reduced spending by consumers. A firm must carefully monitor these factors and devise appropriate strategies to ride out an unfavorable economic environment or take advantage of opportunities arising from an expanding economy. Multinationality, in particular, enables a firm to shift operations among several countries, taking advantage of opportunities or reducing risks as relative labor costs, exchange rates, tax policies, and the like, change.
•
Social-cultural factors, such as the proportion of women in the workforce, health and fitness trends, or the growth in discretionary spending by the young, can also have a significant impact. A firm should monitor these trends to ensure that its products and services meet changing consumer needs. Moreover, as the firm expands into new markets or countries, differences in purchasing behavior due to social and cultural idiosyncrasies must be taken into account.
•
Technological changes have been unprecedented in recent decades in terms of incremental improvements and technological disruptions. Today one can travel faster, communicate instantly globally, and produce in more efficient ways than ever before. New industries have emerged (e.g., cellular phones and dot-coms), while others have entirely disappeared (e.g., typewriters or argentic photo cameras). Businesses
•
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that thrived in the past have often taken advantage of emerging technologies in other industries to serve their customers better or cheaper. 2.2.2
Industry Analysis: Structure–Conduct–Performance Paradigm
A thorough industry and microeconomic analysis must complement the analysis of the macroeconomic environment. Two countries with similar macroeconomic environments may be quite different with respect to their microeconomic profiles. For example, France and Germany are roughly similar in terms of macroeconomic features, but many industries are organized quite differently in the two countries (e.g., the banking system or the waste management industry). Microeconomic analysis enables a better understanding of the overall attractiveness of a particular market and the achievable profitability of the typical firm within that industry. A well-known industry analysis framework is the “structure–conduct– performance” (SCP) paradigm.2 At its core this paradigm assumes a causal link from market structure to firm conduct and performance: market structure determines firm conduct, which in turn determines industry and firm performance. This also works in the reverse direction, since firms may pursue strategies that can alter the market structure (e.g., M&A activities). Government intervention and basic demand and supply conditions may also influence the components discussed above. Market Structure In perfect competition, many firms (facing no entry or exit barriers) populate the market. Moreover clients have a perfect flow of information. Firms offer homogeneous products and behave as price takers. In the long run a competitive industry will supply a good at a price that reflects the marginal cost of the resources required to manufacture that product. On the other extreme, in an industry characterized by a monopoly structure, there are high entry and exit barriers (e.g., large fixed costs), and the customer base is relatively large and homogeneous. A monopolist can earn excess profits stemming from market power as reflected in its ability to set a price higher than the marginal cost of production. Market structure, as proxied by the number and market power of firms in the industry, is an important driver of industry 2. The SCP paradigm was developed by the “Harvard School,” including Joe Bain and Edward Mason, based on empirical observations.
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profitability. The fewer the number of producers in the market, the greater their ability to wield market power and earn excess profits. The structure and bargaining power of suppliers and customers may also influence whether a firm exercises market power. This “extended rivalry” can put significant pressure on the firm. Firm Conduct in the Industry Conduct refers to whether a firm actually exercises its market power to earn excess profits, whereas market structure refers to whether market power is structurally possible in the industry. In addition to individual exercise of market power, firms may collude (explicitly or tacitly) in an oligopolistic industry to sustain a higher price and earn excess profits.3 The ability to exercise market power is influenced by strategic investments, such as engaging in advertisement campaigns or investing heavily in research and development, preventing potential rivals from market entry. Industry Performance The performance of an industry is based on its profitability as well as its static and dynamic efficiency. Excess profits earned by incumbent firms imply that the given industry is not perfectly competitive as some firms wield market power. A secure incumbent may have little incentive to improve its production processes.4 Although a consolidated industry can attain higher static efficiency resulting from larger production scale, the technology employed may not be the most efficient, with firms investing less in R&D compared to a highly competitive industry. In this sense there may be a “cost” to society from consolidated industries since they tend to be less dynamically efficient than a highly competitive industry. This issue has raised a big debate among economists with no clear consensus emerging on whether monopoly and the existence of patents is beneficial or harmful in terms of encouraging innovation.5 2.2.3
Porter’s Industry and Competitive (Five-Forces) Analysis
The industry analysis above provides a benchmark for the profitability of the “average” firm in an industry. A complementary approach to the industry analysis above is Michael Porter’s (1980) “five-forces” 3. Usually a cartel attempts to set a price equal to what a monopolist would charge as cartel members are attempting to maximize joint profits. The stability of a cartel is another matter. 4. A widely known remark on monopolistic industries is that of Hicks (1935, p. 8): “the best of all monopoly profits is a quiet life.” 5. See Tirole (1988, ch. 10) for a comprehensive economic analysis of the incentives to conduct R&D in oligopolistic industry structures.
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Potential entrants Threat of new entrants
Competitors Bargaining power of customers
Bargaining power of suppliers
Suppliers
Customers Rivalry among existing firms Threat of substitute products or services
Substitutes
Figure 2.3 Porter’s “five-forces” industry and competitive analysis From Porter (1980)
framework. Since the early 1980s, it has become a well-known paradigm used both in academe and in practice for analyzing industry performance. According to Porter (1980), the attractiveness of an industry depends on two kinds of strategic interactions: Interactions along the “value chain” These relationships, depicted along the horizontal axis in figure 2.3, involve the relative bargaining power of different parties along the value chain, from suppliers to manufacturers and distributors, to end customers. Extended rivalry This includes the internal rivalry within a particular industry (nature and degree of intra-industry competition) as well as the threat posed to the firm from external forces, such as potential entrants and the threat of substitute products or services. These “extended-rivalry” interactions are illustrated along the vertical axis in the figure. Porter categorizes the relevant economic factors into five groups or forces (hence, the “five-forces” analysis). He proposes a thorough assessment of (1) the impact of these factors on the intensity of competition within the industry or internal rivalry, (2) the threat of substitute products or services, (3) the threat of new firms entering the market, (4)
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the bargaining power of suppliers, and (5) customers. These forces are in turn affected by industry structure and other relevant variables, like entry and exit barriers, product differentiation and availability of information.6 We look at these forces in detail next. Internal Rivalry Internal rivalry is a central piece of Porter’s analysis. It refers to competitive actions taken by each firm to gain market share within the industry. A means to assess the intensity of competition within an industry is to use a concentration ratio. Industrial economists and antitrust authorities have long attempted to find a quantitative measure of the intensity of rivalry using various concentration ratios. A common feature is that they are based on the market shares of the firms already active in the industry.7 Two concentration indexes are most commonly used to measure internal rivalry within an industry: (1) the cumulative market share of the k biggest firms, Ck, and (2) the Herfindhal–Hirschman index, developed by Herfindhal (1950) and Hirschman (1945, 1964), commonly referred to as HHI.8 Cumulative Market Share of the k Biggest Firms (Ck ) Let si denote the market share of firm i in an industry consisting of n active (incumbent) firms. A simple proxy of the concentration in a industry with n active firms is the cumulative market share of the k biggest firms (k ≤ n), that is, k
Ck ≡ ∑ si .
(2.1)
i =1
The market shares of the few “big” players are generally available in market reports. This concentration ratio has a major drawback: it fails to capture heterogeneity among the largest firms. For this reason industrial economists and antitrust authorities generally prefer alternative concentration measures, such as the Herfindhal–Hirschman index. This is discussed in example 2.1 below. 6. Although conceptually very useful, Porter’s (1980) framework does not give a clear prescription as to whether to invest in a given industry. Its primary benefit is that it helps frame an industry and identify key forces affecting market attractiveness. 7. These concentration ratios fail to capture whether external parties can easily enter these markets. 8. Tirole (1988) provides an alternative to these concentration indexes, namely the entropy index that involves a logarithmic function of the market shares. Encoua and Jacquemin (1980) describe desirable properties a concentration index should satisfy and assert that the HHI and entropy indexes are better suited to analyze industry concentration. Here we only discuss the Ck and HHI indexes due to their relative simplicity.
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Herfindhal–Hirschman Index (HHI) The HHI index has the merit of combining information about all firms in the market, not just the five (or k ) biggest firms. The Herfindhal– Hirschman index is given by n
HHI ≡ ∑ si ².
(2.2)
i =1
From a practical viewpoint, it is more difficult to gather information (market shares) on all firms operating in a given industry. Example 2.1 Concentration Indexes Consider the market share distribution of five firms in the three different industry situations described in table 2.1. The last two columns give the values of C 4 and HHI in each case. The industry in case 3 is clearly more concentrated than in case 2, which in turn is more concentrated than in case 1, as the market shares are less uniformly distributed. Using the C4 index, one cannot distinguish the difference in concentration within the industry in case 3 or 2, as the index is equal to 88 percent in both cases. The C4 index does not capture the fact that in case 3 a single firm is dominant with a 50 percent market share, compared to case 2 where the three biggest firms have comparable power. The HHI does a better job since it properly captures this difference, giving a higher value in case 3 relative to case 2. As summarized in figure 2.4, concentration indexes are used by economists and antitrust authorities to identify and differentiate among different market structures. A monopoly is generally characterized by only one seller and very high barriers to entry and exit. An example, from the diamond business, is DeBeers, which has exclusive access to scarce Table 2.1 Concentration indexes based on the first three (C3 ), four (C4 ), or all firms in an industry Case
Market share distribution
1 2 3
1
s1 /5 ¼ ½
s2 /5 ¼ ⅛
1
s3 1 /5 ¼ ⅛
Concentration index s4 1 /5 ⅛ ⅛
s5 1 /5 ⅛ ⅛
C4 80% 88% 88%
HHI 2,000 2,188 3,125
Note: By convention, the HHI is often given as 10,000 times the HHI number given by the definition in equation (2.2).
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Perfect competition
Monopolistic competition
Oligopoly
Dominant firm
Monopoly
0
0 < C4 < 40
40 ≤ C4 < 60
60 ≤ C1 < 90
C1 ≥ 90
HHI < 1,000
1,000 ≤ HHI < 1,800
HHI ≥ 1,800
Figure 2.4 Classification of market structures based on different concentration index ranges
resources giving it a (quasi-) monopoly position due to high barriers to market entry. A monopoly is characterized by C1 ≥ 90 percent. In an industry with a dominant firm and some fringe competitors, the dominant firm can typically ignore strategic interactions with the smaller rivals. Barriers to entry and exit are fairly high. Apple and its iPod® have a dominant position in the portable music industry: despite smaller competitors, Apple has built an uncontestable entry barrier thanks to its unique brand name. Firm dominance is characterized by 60% ≤ C1 < 90% (and HHI ≥ 1, 800). An oligopoly is characterized by few competing firms with 40% ≤ C 4 < 60%. A classic case is the duopoly involving Airbus and Boeing in the airframe industry. Barriers to entry due to technological expertise or huge sunk capital costs are generally very high. National quality standards may also raise entry barriers for foreign competitors and explain oligopoly situations.9 Monopolistic/differentiated competition is characterized by many suppliers with heterogeneous products appealing to different customer or market segments. A good example is the luxury goods industry where distinct brands lure different customer groups (youth, fashion followers, etc.). Product differentiation makes it possible for firms to price products above the marginal cost even if many firms compete in the marketplace.10 Differentiated competition is characterized by 0 ≤ C4 < 40 percent. Perfect competition involves a homogeneous product (commodity) with no single firm having significant market power to influence the price-setting process. The agricultural sector, for example corn production, is considered a good approximation to a perfectly competitive industry. Typically HHI is below 1, 000. 9. A case in point is the French pipeline and valve industry. In France, valves for public water management pipes turn counterclockwise, whereas in most other European countries valves turn clockwise. For this reason only manufacturers that meet French standards are active in France, mostly French companies. 10. In monopolistic competition free entry of new brands leads to zero economic profit with gains from pricing over marginal costs exactly offsetting fixed cost.
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One explanation for the existence of imperfect competition lies with the large fixed costs incurred by incumbent firms: if the market is not sufficiently large, only a limited number of firms can afford incurring these high fixed costs. Even when the market-clearing price exceeds marginal production costs, no external party enters since it cannot recoup its large fixed costs. Threat of Substitutes In assessing the threat of substitute products, a key prerequisite is to determine what the relevant product market is and define the relevant substitute products. Are train and plane substitutes in the transportation market? To travel from France to Cyprus, the train is certainly not an option. However, from Paris to London the train is a valuable option thanks to the Eurotunnel.11 Many economists and antitrust authorities define the relevant market as the smallest set of products for which a firm, should it become dominant, would find it profitable to raise prices significantly (and permanently).12 Once the relevant market is defined, the threat of close substitutes can be assessed. The extent to which a company might be affected by substitute products depends on several factors, including the propensity of buyers to substitute, switching costs, the value added by the company’s product or service in the clients’ perception, as well as the price-performance characteristics of substitute offerings. Potential Entrants Another key force affecting firm performance is the threat of entry. New entrants pose a threat to incumbent firms as higher rivalry generally leads to lower profit margins (negative externality). How easy or difficult it is for a potential entrant to penetrate a market depends on entry barriers.13 Entry barriers may be structural (exogenous) or strategic (endogenous). An example of structural entry barriers is when the incumbent is protected by a favorable government policy or other administrative barriers (e.g., property rights,14 state, market or environmental regulation). 11. This travel option has become more attractive especially after September 11 since it reduces substantially the time to check in and out. 12. To determine the relevant product market, economists utilize various measurement tools, such as cross-price elasticity of demand, price correlation analysis, and shock analysis. 13. Bain (1956) has extensively analyzed entry barriers. A barrier to entry is a mechanism that allows incumbents to make economic profits without threat of entry by competition. 14. Examples include patents, copyrights, and licenses. The property right problem for a social planner hinges on the trade-off between erecting entry barriers (which is not socially optimal) and giving firms incentives to invest in R&D.
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Structural barriers may also stem from other economic phenomena, such as fixed entry costs, economies of scale, scope, or learning, access to scarce resources,15 reputation, network effects,16 product differentiation advantages, process innovation, and vertical integration. Strategic entry barriers involve actions taken by incumbents to deter the entrance of new competitors into the market, for example, by manipulating prices before (limit pricing) or after entrance (predatory pricing), by building excess capacity, or by launching aggressive advertising campaigns to create brand loyalty. They may also create a new brand to capture a previously unsatisfied customer segment (product proliferation). We elaborate on corporate strategies to deter entry later in chapter 4. Bain (1956) identifies three kinds of behavior by incumbents in the face of an entry threat. Entry is blockaded if the incumbent is well protected by insurmountable structural barriers to entry and exit so that it continues to enjoy incumbency profits. The incumbent can ignore the threat of entry so that strategic interactions play a minor role in this case. Entry is deterred if the incumbent is not exogenously protected and behaves strategically to make it unprofitable for new competitors to enter. For instance, by massively investing in automated production processes and reducing its production costs (a strategic first-stage investment), a firm may ensure that the rival’s post-entry profits are driven close to zero. This kind of deterrence strategy may be extremely expensive to pursue, but nonetheless strategically justified. Entry is accommodated when the incumbent finds it preferable to allow entry than to erect costly barriers. A strategy of entry accommodation is not tantamount to a passive stance toward one’s rivals: the incumbent can still build up an early competitive advantage (e.g., via excess capacity or strong brand image). Bargaining Power of Suppliers and Customers Both suppliers and customers can extract profits from manufacturers depending on who influences the price-setting process when the manufacturer buys or sells. The firm’s ability to influence or set prices depends on a number of factors: The concentration and market power of customers and suppliers. If the customer base is small (e.g., B-to-B clients) but suppliers are numerous, •
15. A case in point is Nutella. The firm has an exclusive right to acquire a given species of nuts, so no entrant can imitate Nutella’s unique taste. 16. The combination “PC + MS DOS” won the computer war against “Apple Mac OS” partly because IBM had a less restrictive licensing policy than Apple.
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customers will wield more bargaining power. L’Oréal, one of the major cosmetic companies worldwide, puts significant pressure on its packaging services suppliers, which have to toe the line. •
The price elasticity of demand for the product (inputs or outputs).
•
Customer and supplier switching costs (purchase from another party).
The ability of customers to backward integrate or of suppliers to forward integrate. •
• The relation specificity of the firms’ assets (specific to clients or suppliers).
2.3
Creating and Sustaining Competitive Advantage
The profitability of a firm depends both on industrywide characteristics (e.g., competitive pressures, market power, bargaining power) as well as on firm-specific attributes. We already discussed the main strategic management frameworks for assessing industry attractiveness. We next focus on firm-specific factors that might help explain why certain firms in a given industry are more successful than the “average” firm considered in industry analysis. We review the “generic strategies” proposed by Michael Porter (1980), namely the incentive to be a cost leader, a differentiation leader, or focused on a specific customer segment. To understand these concepts from a microeconomic viewpoint, it is necessary to first address the notion of value creation and competitive advantage.17 2.3.1 Value Creation To earn profits in excess of the industry average, a firm must strive to be superior to its competitors in the industry. A key to superior performance is creating more value for customers than one’s rivals by exploiting some competitive advantage. Firms’ strategies aim to create such comparative advantages. To assess the value created by a firm, we need to go back to basic microeconomics. For each product there is a maximum value the consumer is willing to pay. This maximal value (“willingness to pay” or “consumer value”) determines whether the customer purchases the product. If the price asked for the good exceeds the customer’s willingness to pay, the customer will reject the product. If the price is lower, the customer will purchase it, “earning” the difference (between the 17. For a more detailed analysis of how firms can create competitive advantage, refer to Besanko et al. (2004, ch. 11) and Grant (2005, chs. 8 and 9).
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customer value and the product price) as consumer surplus or utility. If p denotes the price and u the consumer’s willingness to pay (utility), the consumer surplus is represented by u − p if p ≤ u, and 0 otherwise. If the product is homogeneous, meaning the products offered by rivals are considered perfectly identical, a rational consumer will purchase from the lowest price supplier. For instance, if a consumer sees no significant difference among airline carriers and does not care much about small differences in the time schedules, she would choose to fly with a low-cost airline like Ryanair. We must also consider the incentive of a given firm to sell in the marketplace. This incentive is captured by the producer surplus or economic profit, and represents the bottom-line criterion for many managerial decisions. The value created by the producer is the difference between the price at which the firm sells the good, p, and its unit cost of producing it, c.18 The total economic value created through such an exchange in the marketplace is the difference between the utility the product brings to the consumer and the amount it costs to be produced, namely u − c. A portion of this value is captured by the consumer in the form of consumer surplus, u − p, and the remaining part is earned by the producer as economic profit, p − c. That is, Total value created = consumer surplus + producer surplus u−c = + (u − p) ( p − c)
(2.3)
The relation above is depicted in figure 2.5. A necessary condition for market exchange to take place is that the value (utility) the product represents for the consumer (u) exceeds the cost of producing it (c). In this case both the consumer and the producer gain. In equilibrium the producer does not set the price arbitrarily; this choice is closely related to the consumer price elasticity of demand, the production cost, the nature and degree of competition, as well as the size of the customer base. In a perfectly competitive market all economic value created by the product offering is captured by the consumer and competing firms make no economic profit. In order to be economically profitable, firms need to find ways to deviate from perfect competition. A firm has a competitive advantage when it is able to deliver more economic value than its rivals. 18. Included in c are the labor costs, input prices, and cost of capital per unit of the product. The profit and value measures we discuss here are net of the opportunity cost of capital. The term used for profits in this case is “economic profit.” A financial translation of this economic concept is economic value added (EVA).
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Consumer value u Consumer surplus (u−p) Price p Producer surplus (p− c)
Total economic value u− c
Cost c
Figure 2.5 Total value created consisting of consumer surplus plus producer surplus
In reality, if consumers are not identical, a given product may represent a higher utility for one consumer than for another. Distinct market segments can be identified in which consumers (with uniform preferences) are more willing to accept one particular offering versus another, namely the one for which their consumer surplus (u − p) is the highest. This basic economic principle underlies market segmentation. To create higher economic value, firms pay considerable attention to what drives customer value and production costs. The following factors typically drive the customer’s willingness to pay: The appeal of physical characteristics and the perceived fitness of the product (e.g., the size of furniture may be an important criterion if one’s flat is only 20 square meters or 200 square feet). •
The quality of services associated with the product (the price premium one may be willing to pay for a night at Sofitel in NYC rather than the Best Western Hotel is linked to the perceived services one receives rather than the quality of the bed). •
Brand name (Apple®’s brand name creates a unique competitive advantage for the iPhone® in the now-commoditized mobile telephony market). •
•
Perception by customers.
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Firm infrastructure (e.g., finance, accounting, legal) Human resource management
Support activities
Technology development Procurement
Primary activities
Inbound logistics
Production
Outbound logistics
Marketing and sales
Services
Main activities
Figure 2.6 Porter’s value chain From Porter (1980)
The following factors typically drive production costs: Drivers related to the size of the firm (economies of scale and scope, capacity utilization and bargaining power). •
•
Experience of the firm (learning-curve effects).
Factors related to organizational structure (organization of transactions, agency efficiency, vertical chain). •
•
Other costs (lower input prices, production in a low labor cost country).19
To understand how value is created along the vertical chain, Porter (1980) proposes a conceptual framework (shown in figure 2.6) involving the organization of value-creating activities referred to as the value chain. Porter’s value chain essentially divides the organization into a series of value-creating activities that can be broadly classified as supportive or primary. Regardless of whether an activity belongs in the first or the second category, it can potentially contribute to creating more economic value either by increasing the value perceived by the consumer or by reducing the cost of producing it. Primary activities are involved with the physical creation of the product or development of the service until it reaches the end customer. Inbound logistics, production or operations, outbound logistics, marketing and sales, and services are typical primary activities. Additional activities support these primary activities by providing procurement and technology services, human resources management, and various firmwide functions such as finance, accounting, and 19. For more details on benefit and cost drivers, see Besanko et al. (2004, pp. 411–18).
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legal services. Increased value added may also be a result of these firmwide functions. The organization as a whole is embedded in a larger stream of activities that include suppliers, distributors, and buyers’ value chains, which together with the firm’s value chain form the so-called value system. These activities are all interrelated, so events in one of them may affect the company’s overall strategy basis. The notions of value creation and value chain are essential for understanding the “generic competitive strategies” proposed by Porter (1980). 2.3.2
Generic Competitive Strategies
A generic competitive strategy refers to how a firm positions itself to compete in the marketplace. Porter (1980) identifies three generic strategies—cost leadership, differentiation, and focus strategy. The choice of one strategy over another partly depends on the size of the market the firm wants to cover. When covering a broad market, firms can mainly pursue two generic strategies: cost leadership or differentiation. In homogeneous goods markets, cost leadership can play a central role since customers primarily decide based on the price. In a market where customers prefer customized products, a benefit leader or differentiated firm that offers a perceived superior product may obtain a stronger competitive advantage. A third strategic alternative is to specialize in a narrow customer segment, namely develop a niche, following a focus strategy. Cost Leadership In homogeneous goods markets, cost leadership consists in offering similar products as rivals but at lower price. This strategy typically involves producing larger quantities and appealing to a broader market (broad scope). It is more likely to succeed when customers are sensitive to prices, as measured by the price elasticity of demand. A costleadership strategy is advisable when products are commodity-like or customer services are hard to differentiate. Cost advantage can result from economies of scale, scope, or learning-curve effects. Economies of scale offer unit-cost savings that increase with the level of output: the average production cost of a single product decreases with the number of units produced. Fixed costs here play a critical role: when volume rises, the fixed-cost component per unit declines. Such savings may have a material impact on the equilibrium market structure. If fixed costs are large, only a limited number of firms can profitably operate in the market: the price markup over the marginal cost needs to be sufficiently large to justify spending the fixed costs by the incumbents. Economies of scope
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correspond to cost savings gained by operating several product lines or businesses: the production of good A reduces the production cost of product B. Learning-curve effects are related to the experience and know-how firms cumulate over time when they produce a given product or provide a certain service. The classic BCG matrix originated from work related to the learning-curve effect.20 Cost leadership can also result from improved process efficiencies, increased bargaining power with suppliers, higher capacity utilization (especially when fixed costs are high), lean organizational structure, better compensation systems, or location advantages (manufacturing in low-cost countries). Firms employ several approaches to achieve cost leadership: cost leadership under benefit parity, cost leadership under benefit proximity, and product redesign. In case of benefit parity, the cost leader offers a product identical to the products offered by competitors but the cost of producing it is significantly lower. In case of benefit proximity, the cost of producing the good is lower but the consumer perceives the product to be of lower quality; here the increase in consumer surplus due to the lower price offsets the quality decrease. Alternatively, a firm may offer a product to consumers that is qualitatively different from competitors’ offerings (product redesign). Whether achieving cost leadership enhances firm value also depends on the size of the initial investment. Differentiation Strategy Consumer preferences in the marketplace may not be homogeneous. Within the same market some customers may have preferences for specific product features and others for other features. A differentiation strategy enables a firm to create more economic value than its rivals by offering higher consumer value than other products supplied in the marketplace. In exchange for this higher consumer surplus, a consumer may be willing to pay a price premium. A differentiation strategy may encompass the whole value chain. This strategy is more appealing when price elasticity is low or when cost advantages are limited. Traditionally one distinguishes two kinds of differentiation. In case of horizontal differentiation, product differentiation can be achieved thanks to new combinations of product characteristics (e.g., MP3-player for mobile telephone handsets), new distribution or sales outlets (e.g., online shopping for people living on the fast lane), better marketing differentiation (lenovo® laptop, mainly focused on corporate customers), 20. The BCG matrix paradigm asserts that a conglomerate should finance promising “stars” with excess cash generated by “cash cows.” By so doing, the “stars” commence production early on and accumulate experience ahead of competitors, leveraging on learning-curve effects.
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or better complementarities owing to network effects (e.g., Blu-ray players on Sony’s PlayStation3®).21 Vertical differentiation is achieved when all perceive the product as being of higher quality. Customers without budget constraints would be willing to pay a premium for it. Again, three cases may be distinguished here. In case of cost parity, the cost of producing the good remains unchanged, but the differentiated product is perceived as being of higher quality by a given customer segment. In case of cost proximity, the differentiated product implies higher production costs (than the standard product), but consumers appreciate the higher quality and willingly pay a price premium that compensates for these higher costs. Product redesign consists in offering a product with new characteristics that appeal to new or existing customers. Focus Strategy Alternatively, a firm can devise a focus strategy and tailor its products and services to the requirements of a specific customer group. In doing so, the firm creates higher economic value for this specific segment. One type of focused strategy is to be a local player or appeal to a given population group (e.g., Mecca Cola® as an alternative to CocaCola® and Pepsi® for Muslim consumers). A key advantage of a focused strategy is that the targeted market may not be large enough to accommodate many producers, enabling the focused firm to enjoy a quasi-monopolistic position within its segment. 2.3.3
Sustaining Competitive Advantage
It is said that history repeats itself in cycles. Many once-leading firms have been dethroned abruptly for not being able to protect and sustain their once-formidable competitive advantage. Imitators and new entrants with superior technologies can quickly erode competitive advantages that have taken years to build. Firms that persistently outperform their peers are those able to sustain or renew their competitive advantage over the long run. Sustainability of competitive advantage invariably depends on the dynamics of the market. In a market with low entry and exit barriers, it is more likely that a firm will enter when it sees a window of opportunity. To avoid this, an incumbent can erect entry barriers. Just as there are various mechanisms to protect the profitability of an industry (e.g., erection of entry and exit barriers), there exist mechanisms within an industry to protect a firm’s competitive advantage from rivals’ 21. See Besanko et al. (2004, p. 415).
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imitation. A firm’s ability to create and sustain a competitive advantage depends on its firm-specific resources and the distinctive capabilities arising from these resources. To keep competitors from duplicating its competitive advantage, the firm should constantly strive to create or enhance asymmetries by acquiring and managing distinctive resources and capabilities that cannot be readily duplicated by would-be imitators. Heterogeneity among firms is the cornerstone of the resource-based view of the firm, emphasizing firm specificity and uniqueness. Imitation can only be precluded if there is heterogeneity and imperfect transferability of key resources. Besides the scarcity and immobility of unique resources, firms can erect isolating mechanisms to prevent rivals from internally developing similar resources. Isolating mechanisms fall into two main categories: (1) barriers to imitation (e.g., property rights or exclusive access to scarce resources) and (2) early-mover advantages, such as learning-curve effects and brand-name building. When a firm cannot acquire distinctive key resources, it has to build them up over time. In its history of investment and commitment, a firm accumulates skills, assets, and resources involving path dependencies. The experience, relationships, and reputation a firm has built over time enable it to leverage its resources and capabilities and represent sources of uniqueness. It is the exploitation of firm-specific resources and capabilities in a dynamic way that enables a firm to enjoy higher (excess) profit flows. Apple®, frequently ranked as one of the most famous brands in the world, has successfully leveraged its brand image to sustain its profitability over time. Conclusion In this chapter we provided a brief overview of the main paradigms and issues in strategic management, focusing on how prevalent frameworks view the key drivers of market attractiveness and firm profitability. We elaborated on market structure and discussed how limited access to a market and the existence of entry barriers can enhance a firm’s profitability. We described Porter’s five-forces paradigm and how it can help structure market analysis. We also discussed how, within a given industry, a firm can build competitive advantage through production efficiency (cost leadership), branding (differentiation leadership), or a focus strategy. Finally, we discussed the importance of sustaining competitive advantage dynamically.
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Selected References Bain (1956) examines barriers to market entrance in detail. Porter’s (1980) seminal work on industry and competitive analysis is a cornerstone for understanding the art of strategy and competitive advantage. Besanko et al. (2004) provide an easy-to-read economic perspective on strategy. Bain, Joe S. 1956. Barriers to New Competition. Cambridge: Harvard University Press. Besanko, David, David Dranove, Mark Shanley, and Scott Schaefer. 2004. Economics of Strategy, 3rd ed. New York: Wiley. Porter, Michael E. 1980. Competitive Strategy. London: Macmillan.
3
Market Structure Games: Static Approaches
Studying industrial organization is useful to deduce managerial insights to help explain how firms should behave strategically when faced with competition. In this chapter and the following one, we review some basic principles and models in this area, discuss industry structures, and examine information asymmetry, commitment, and collaboration. Basic ideas developed in these chapters serve as building blocks in subsequent discussions. In this chapter we deal mainly with static models that help explain the modus vivendi in competitive situations when firms focus on the short-run impact of their decisions. We discuss simple economic models that characterize optimal firm behavior under different industry structures. Chapter 4 extends the discussion to include long-term, dynamic strategy formulation, helping explain phenomena such as restraining one’s own freedom (commitment) or collaboration among rival firms. This chapter is organized as follows. Section 3.1 discusses the benchmark case of a monopolist, developing the basic building blocks for subsequent models. We then turn to oligopolistic situations where firms have enough market power to influence the decisions of rivals. Such analysis requires the use of game-theoretic tools. Section 3.2 introduces static models of duopoly, presenting first the case of Bertrand price competition and then discussing the classic Cournot quantity duopoly model. Section 3.3 extends the standard Cournot quantity competition analysis to accommodate a larger number of firms in oligopoly, obtaining the market equilibrium in perfect competition as a polar case. Going beyond earlier models that involved complete information, section 3.4 explores the impact of incomplete information on market structure in a duopoly.
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Monopoly
In monopoly entry barriers are typically sufficiently high to shield the incumbent firm from competitive entry (blockaded entry). Customers take price as given and cannot influence the price-setting process. Monopoly may naturally arise when demand is not sufficient or fixed costs are too high to accommodate other incumbents (natural monopoly) or when regulation prohibits further market entry (e.g., patents).1 In western Europe the electricity market used to be considered a national natural monopoly because of the prohibitive fixed costs. Price setting in a competitive market leads to lower prices, making it difficult or impossible for more electricity utilities to make a profit once large fixed costs are considered. The equilibrium market structure critically depends on such fixed costs. Economies of scale and scope are thus one of the main drivers of industry structures (Viner 1932). When large-scale production plants are needed (e.g., nuclear power plants), a monopoly is economically justified and may be socially preferable.2 In this chapter we simplify and ignore fixed production costs, focusing instead on the relationship between the market-clearing price and marginal production costs.3 In perfect competition a firm takes the market price as given and produces until the marginal cost of an additional unit is just below the market price. In contrast, a monopolist is a price setter. Consider a monopolist facing (inverse) demand function p (⋅) and variable production cost C (Q), as a function of the quantity Q supplied in the marketplace. Given a downward-sloping demand (∂p ∂Q < 0), the monopolist faces a trade-off between price and quantity. Setting a higher price p implies earning a higher profit margin but selling a lower quantity. Inversely, if the monopolist sells an additional unit of output, it moves down the (inverse) market demand curve, suffering a (marginal) price reduction. This trade-off is linked to marginal revenues. The monopolist’s profit function is concave in its own quantity, Q,
π (Q) = R (Q) − C (Q), 1. A natural monopoly may exist when a firm can make excess economic profits when operating alone, while a duopolist cannot. If π (i) indicates the equilibrium profit of a firm in an industry with i incumbent(s), π (2) < 0 < π (1) holds for natural monopoly. 2. Today, following deregulation, the electricity market is sufficiently mature to accommodate more suppliers. 3. Under certain conditions we show that firms can make excess economic profits (gross of fixed costs). Considering fixed costs might invalidate some of these results.
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where total revenues are given by R (Q) = p (Q) × Q. To earn maximum profit, the firm must choose to produce that output, Q *, that equates its marginal cost, MC (Q) ≡ C ′ (Q), to its marginal revenue, MR (Q) ≡ R ′ (Q).4 This principle, MR (Q *) = MC (Q *), holds whatever the industry structure and the assumed profit function. In perfect competition, the marginal revenue equals the price (since the output decision of an individual firm does not affect the price-setting process, p = MC in equilibrium). In monopoly, since the firm’s output decision influences the market-clearing price and ∂p ∂Q < 0, the firm loses revenue on already produced output when it decides to produce more. Box 3.1 and figure 3.1 describe common types of demand functions. Consider the linear (inverse) demand function p (Q) = a − bQ
(3.1)
and a linear cost function C (Q) = cQ with c < a, b > 0. In this case the marginal revenue is MR (Q) = R ′ (Q) = a − 2bQ and the marginal cost is MC (Q) = C ′ (Q) = c. In monopoly we thus have a − 2bQM = c. The equilibrium quantity produced by the monopolist is QM =
a−c . 2b
(3.2)
The equilibrium price, corresponding to point E′ in figure 3.2 (determined by substituting equation 3.2 for QM into the inverse demand function 3.1) is pM =
a+c a−c = c+ 2 2
(> c ) .
(3.3)
From the equilibrium quantity and price, equations (3.2) and (3.3), the equilibrium profit for the monopolist is 4. The marginal benefit (revenue) is MR (Q) ≡ R′ (Q) = p (Q) +
∂p (Q) × Q. ∂Q
The profit function is concave so the first-order condition is both sufficient and necessary for a maximum output (Q *) to obtain. The first-order derivative of the profit function is ∂π ∂p (Q) = p(Q) − C ′(Q) + (Q) × Q . ∂Q ∂Q The first two terms yield the profitability of an extra unit of output (i.e., the difference between the price and marginal cost), while the third term recognizes that an increase in output (implying a decrease in ∂p ∂Q < 0 ) affects the profitability of units already produced.
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Box 3.1 Common demand functions
Industry structure models rely on certain assumptions. One key assumption relates to the (inverse) demand function used to determine marketclearing prices and firm profits. We assume that firms can observe customer demand. There are several deterministic inverse demand functions. The most widely used are the following: The linear demand function, p(Q) = a − bQ, where p (Q) is the marketclearing price, Q the total quantity produced by all firms in the industry, b > 0 , and a is the demand intercept. Figure 3.1a depicts this linear demand function. Here p(·) is downward sloping (with slope − b < 0 ), indicating that for an increased industry output ( Q) the market-clearing price ( p (Q)) declines. This property holds for most markets (with notable exception the luxury market, where a high price may signal a more exclusive product). One limitation, however, is that for the price to be (or remain) positive, total industry output must be limited to a certain range (if Q > a b, the price is negative). • The isoelastic demand function, p(Q) = 1 (Q + W ), where W is a positive constant. This curvi-linear demand is depicted in figure 3.1b. Here •
Price (p)
Price (p)
a
p(Q) =
p(Q) = a−bQ
1 Q+W
Slope − b 0
a /b
0
Quantity (Q)
Quantity (Q)
(a)
(b)
Price (p)
Price (p)
p(Q) = Q−1 ⁄ ep p(Q) = a × exp(−e p Q) 0
(c)
Quantity (Q)
0
Quantity (Q)
(d)
Figure 3.1 Different (inverse) demand functions (a) Linear demand; (b) isoelastic demand; (c) exponential demand; (d) constantelasticity demand
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Box 3.1 (continued)
whatever the total industry output Q, the market-clearing price remains positive. • The exponential demand function, p(Q) = a exp ( − ε p × Q), where ε p is the price elasticity of demand. This is illustrated in figure 3.1c. This steeply declining demand function has the property of excluding negative prices even for very high quantity levels and allowing a limited price for low output. −1 • The constant-elasticity demand function, p (Q) = Q ε p . This demand function is often used because of its relative simplicity. It is depicted in figure 3.1d. A condition on the price elasticity parameter ε p is often imposed to obtain nice mathematical properties. Since the linear (inverse) demand function is the simplest and most widely used, many industrial organization models rely on it to obtain simple expressions for equilibrium quantities, prices, and profits.
Price (p)
a Equilibrium price
pM =
E'
a+c 2
E
c
Marginal cost (c) Demand function
p(Q) = a−bQ
Marginal revenue
MR(Q) = a−2bQ
−2b
0
Equilibrium quantity
QM =
a− c 2b
Figure 3.2 Equilibrium price and quantity in a monopoly
−b
Quantity (Q)
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πM =
(a − c ) ² 4b
.
(3.4)
As confirmed in figure 3.2, the monopolist firm maximizes its profit by selecting the output that makes its marginal revenue, MR (Q), just equal to its marginal cost, c, as represented by the intersection point E. Inverse Elasticity Markup Rule or Lerner Index Lerner (1934) complements the discussion on price setting by a monopolist by adopting an approach based on the price elasticity of demand, ε p. This elasticity measure corresponds to the percentage decrease in quantity sold due to a 1 percent increase in price, all other things equal. It is given by
H p (Q ) ≡ −
∂Q Q pQ . =− ∂p p ∂p ∂Q
(3.5)
From the profit-maximizing condition, a markup formula measuring the firm profit margin in equilibrium obtains as5 L≡
pM − c 1 = . εp pM
(3.6)
This markup rule, commonly known as the Lerner index, provides a practical rule of thumb for pricing a product in a monopoly. It asserts that the profit margin received by a monopolist in equilibrium, L, is inversely proportional to the price elasticity of demand, ε p ≡ ε p(QM ). If customers are highly sensitive to a price increase (i.e., ε p is high) and would refrain from purchasing the product in case of a price increase, the firm’s profit margin L will be low, since ∂L ∂ε p < 0. In contrast, if the price elasticity of demand is low such that customers are hardly sensitive to the price increase, the monopolist can set a very high price ( pM ) and still enjoy a high profit margin (L). The price distortion is larger when customers reduce their demand only slightly in response to an increased price. Table 3.1 gives an indication of price-elasticity ranges. 5. We can rewrite the marginal revenue in note 4 based on the price-elasticity formula (3.5), obtaining MR (Q) = p +
∂p (Q) × Q = ∂Q
⎛ 1 ⎞ Q ∂p ⎛ ⎞ p⎜1 + (Q)⎟ = p ⎜ 1 − . ⎝ ⎠ ⎝ ε p (Q) ⎟⎠ p ∂Q
Expression (3.6) obtains from the first-order condition in the special case of a linear cost function. If demand is linear as in (3.1), it obtains from (3.3) that L = (a − c ) (a + c ) and ε p = (a + c ) (a − c ), giving L ≡ 1 e p.
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Table 3.1 Range of price elasticity of demand Perfectly elastic
εp → ∞
Elastic
1 < εp < ∞
Unit elastic
εp = 1
Inelastic
0 < εp < 1
Perfectly inelastic
εp = 0
Example 3.1 Market Equilibrium in Monopoly Assume a linear demand as in equation (3.1) with parameters a = 10 and b = 1; that is, p (Q) = 10 − Q. The marginal cost is constant, c = 1, and the profit function is π (Q) = ( p (Q) − 1) × Q. In this case equations (3.2) to (3.6) give a − c 10 − 1 a + c 10 + 1 (a − c ) = = 4.5 , pM = = = 5.5 , π M = = 20.25, 4b 2b 2 2 2 2
QM =
e p (QM ) = −
pM / Q M 5.5 = − ( −1) = 1.22 , 4.5 ∂p / ∂Q
and L=
pM − c 5.5 − 1 4.5 ⎛ 1 ⎞ = = = . pM 5.5 5.5 ⎜⎝ ε p ⎟⎠
Figure 3.3 illustrates the price-setting trade-off (higher profit margin vs. lower sales) that occurs in this situation and the optimal price markup using the Lerner index. 3.2
Duopoly
A duopoly is characterized by competition among two firms in an industry. The analysis of duopoly or oligopoly situations requires a clear depiction of the influence of each firm on the other firms in the industry. An appropriate tool kit is offered by game theory that gives prediction about the likely outcome of such strategic interactions. A game has a certain structure, rules, and characteristics as discussed in box 3.2. The tools borrowed from game theory, used throughout the book, are sometimes criticized for being based on the strong assumption of rationality by all agents. In box 3.3 Reinhard Selten addresses
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Demand function, marginal revenue
Profit 25
12
5.5−1 1 L≡ ≈ 5.5 1.22
a =10 10
πM = 20.25 20
8 Equilibrium price
pM = 5.5
E'
6
Demand function
15
Profit function
p(Q)=10−Q
10 4 Marginal revenue
5
MR(Q)=10−2Q
2
E
c=1
−1
0 0
2
4 Equilibrium quantity
6
8
QM = 4.5
0 10
Quantity (Q)
Figure 3.3 Trade-off and optimal price markup for a monopolist The demand function is p (Q) = 10 − Q and marginal cost is constant ( c = 1).
Box 3.2 “Rules of the game”
The players These are the individuals, firms, entities, or actors who make decisions. In games under exogenous uncertainty, a player’s actions depend not only on the strategies played but also on external events, called “states of the world.”a Alternative actions and strategies Each time players are called upon to “play” (possibly only once), they face different alternative actions they can choose from. The collection of alternative actions at a given stage is called the action set. It is either discrete (e.g., involving choices whether or not to enter a market) or continuous (e.g., a decision on the output to supply or the size of the production capacity expansion). A strategy is a contingent a. Occasionally so-called pseudoplayers, such as nature, are used to account for or explain outside exogenous factors, such as R&D success or demand realization. Nature selects the state of the world at random regardless of the players’ actions. The probability distribution of nature’s moves is a key element of certain games, especially in option games modeling demand uncertainty.
Market Structure Games: Static
Box 3.2 (continued)
plan of actions indicating which action to take at each and every stage or state.b Information set Players condition their actions on the information they possess. An information set consists of all relevant information available to a player at the time of a decision.c Over time players generally collect more information. A key question is whether players can react to information revealed over time or if they stay committed.d Payoff structure Each strategy combination, also called strategy profile, results in a specified payoff value for each player. In equilibrium, each player pursues a strategy that maximizes its expected well-being. In a business context, ignoring agency problems, this corresponds to managers acting to maximize shareholder value. Order (sequence) of decisions In a simultaneous game, all players make their decisions at the same time so that no player observes other players’ actions before making its own decision.e If one player makes its decision after the other, having observed the earlier actions, we face a sequential or dynamic game. In such games, “time” is interpreted in terms of decision time, not necessarily real time.f b. At the start of the game, each player determines, as part of a contingent rule, what to do at each subsequent decision node, conditional on information available then. A strategy prescribes action choices at decision nodes that might not be reached during the actual (equilibrium) evolution of the game (i.e., decision nodes off the equilibrium path). A strategy profile encompasses the strategies pursued by all the players. The distinction between actions in one-stage problems and strategies as a contingent plan of actions in dynamic problems is essential to the understanding of dynamic games. c. One often distinguishes various types of information structure, such as perfect, incomplete, or imperfect information. Under perfect information, decision makers know the previous moves over the play of the game. Under incomplete information, players do not know the exact payoffs received by rivals. Such games are typically transformed into games of imperfect information by assuming that players have probabilistic information (beliefs) about some characteristics (types) of their rivals (Harsanyi transformation). d. In dynamic games, strategies that depend on calendar time but not on previous plays are called open-loop. Strategies that take account of previous plays as well are closed-loop. This distinction can substantially affect the resulting equilibrium outcome. We come back to this distinction later when discussing commitment and games of timing. e. When competing firms face such a problem under perfect information, each is aware that there is another player that is similarly aware of the potential choices of the former and the impact of its decisions on their payoffs (common knowledge). f. A two-period game where the second player faces information asymmetry concerning the move of the first player is tantamount to a simultaneous game.
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this issue and suggests how to refine the standard approach by incorporating behavioral considerations. There are two archetypical economic models of duopoly competition, Cournot quantity competition and Bertrand price competition. These two basic models serve to illustrate how competitive advantage can result from a better cost position or from product differentiation. They thus provide an economic foundation to Porter’s (1980) generic business strategies and enable an economic study of strategic management issues via option games. Cournot (1838) first introduced the quantity competition model. The original treatise did not explicitly rely on game theory, but Cournot (1838) roughly anticipated the equilibrium concept developed by Nash (1950) over a century later. A half century after Cournot presented his analysis of quantity competition, Bertrand (1883) criticized the model’s fundamental premise, arguing that in the real world firms do not compete in quantity but in prices. The basic Cournot model essentially assumes that each individual firm sets the quantity it wishes to provide to the market, and once all delivered quantities are aggregated, the market (acting as a “representative auctioneer”) determines the market-clearing price given the total quantity supplied by producers and the demand sought by consumers. In contrast, rather than assuming that the market price is exogenously determined, Bertrand posited that individual incumbent firms directly set their prices. The theory of industrial organization has since largely relied upon these two cornerstone models. Apart from the early criticisms of these models, economists have interpreted (for reasons explained later) Bertrand price competition as a short-run, tactical approach, while Cournot quantity competition is thought to involve long-term capacity commitments. For this reason we first present the basic Bertrand model where identical firms compete in price over homogeneous products. This model leads to what is called the Bertrand paradox, describing a situation where, due to competitive pressures, duopolists make no excess profits at all (a fortiori if fixed costs are material). Subsequently, we discuss a refinement allowing firms to produce differentiated products (differentiated Bertrand model). This extended model gives interesting insights for understanding the incentive of firms to differentiate their product offerings from those of rivals, avoiding cutthroat competition. We next consider the basic Cournot model of duopoly where firms compete in quantity
Market Structure Games: Static
Box 3.3 Interview with Reinhard Selten, Nobel Laureate in Economics (1994)
1. Your work on game theory refined existing solution concepts to select equilibria more in line with intuitive prediction. Do you think there is room for heuristics in economic analysis? My work on equilibrium refinement was guided by rational game theory and intuitions based on this approach. I think that rational game theory has to be complemented by a descriptive approach. This leaves room for heuristics but of a different kind. These heuristics involve plausible assumptions about nonoptimizing decision procedures that are within the boundaries of human cognitive abilities. 2. You are a pioneer of experimental economics. Do you believe that experimental economics has revealed limited usefulness or relevance of mathematical modeling in economic analysis? Experimental economics is very important for the development of a behavioral approach of decision and game theory. Mathematical methods are also needed for that purpose, but they are different from those used in the mainstream of economic theory. They might involve the analysis of dynamic systems, for example, learning processes and stationary concepts replacing game equilibrium. 3. Game theory generally hinges on the assumption of rationality by all players. What if people do not react rationally? Do you believe that the use of rational game theory in social sciences is necessary or appropriate? Rational game theory assumes common knowledge of the rationality of all players. When rationality is defined in terms of Bayesian decision
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Box 3.3 (continued)
theory, human behavior is not rational in this sense but it is not irrational. Therefore it is necessary to build a theory of bounded rationality. However, this task is far from being completed, and as long as we do not have a good substitute, rational game analysis may still be valuable because it reveals the strategic structure of a problem and may serve as a benchmark for experimental economics. We must be very skeptical, however, about the descriptive validity of rational decision and game theory.
facing the same production cost and subsequently extend the analysis to allow for (variable) cost asymmetry. 3.2.1
Bertrand Price Competition
Here, to analyze strategic interactions based on the Bertrand model of price competition, we distinguish between two cases, depending on whether firms offer identical products or not. Standard Bertrand Model Consider first a market where two symmetric firms compete over a homogeneous product, have no capacity constraint and can supply whatever quantity is required. Firms decide what price to set, and customers decide to purchase the product if the set price is lower than the value they attribute to the product (their willingness to pay). As the products offered by the rival firms are not distinguishable (they are perfect substitutes), customers have an incentive to purchase from the firm with the lowest price. Suppose that firm i sets a price pi. The best response for firm j is to set a price pj just lower than its rival’s, namely pj = pi − ε, where ε (> 0) is small, as long as its profit margin is nonnegative. Similarly firm i will then consider selling its product at a somewhat lower price pi − 2ε (< pj ) if this price is still higher than (or equal to) its marginal cost of production. Firm j will likely follow suit, and so on and on. Eventually, the rival duopolists will reduce the price they set all the way down to the marginal cost of production (assumed identical for both firms at a constant level c), at which point they stop further pointless price reduction. Consequently the duopolists will make no excess economic profit, a result analogous to the outcome found in perfectly competitive markets.
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Thus, according to the standard Bertrand model of price competition, there is no need for a large number of players to be active in an industry to create the economic conditions most beneficial to customers (i.e., perfect competition). Two firms competing in price over an undifferentiated product suffice to create a situation involving no excess economic profit for any firm and the highest possible consumer surplus. This outcome has been coined the “Bertrand paradox” in industrial organization since it challenges common business sense. One may wonder why firms bother to enter the market if they make no economic profit once they enter. A firm typically incurs a fixed cost upon entry. If a firm already operates, the other firm will not invest, however small the fixed entry cost; the market is thus likely to yield a monopoly. This standard Bertrand model rests on strong assumptions that can be relaxed but derives its appeal by illustrating cutthroat competition among a few firms. Porter’s business strategies are essentially meant to circumvent such undesirable outcome, allowing for firms to earn excess profits. To achieve this, firms compete over dimensions other than price. Differentiated Bertrand Model One noted business strategy is for firms to differentiate their product offerings. This is the differentiation strategy proposed by Porter (1980). Economists can justify this strategy thanks to the differentiated Bertrand model. Following Gibbons (1992), suppose that two firms, i and j, produce a differentiated product while facing symmetric (linear) cost6 Ci (qi ) = cqi, where qi is the quantity produced by firm i, with constant marginal cost of production c. The (modified) linear inverse market demand function for each differentiated good (allowing to charge a different price by firm i, pi) is given by pi (qi , q j ) = a − b ( qi + sq j ),
(3.1′)
where a > c ≥ 0, b > 0, and parameter s ∈ [ 0, 1) represents the substitution effect between the two differentiated product offerings.7 Products are unrelated if s = 0; here each firm can disregard the rival’s price decision since it has no impact on its own, with demand function (3.1′) reducing to the linear demand in (3.1) faced by a monopolist. If s approaches 1, 6. We formulate the demand function somewhat differently than Gibbons (1992). 7. The substitution effect captures the price change of one product for a unit change in the supply of its substitute product.
88
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products are undifferentiated or perfect substitutes; equation (3.1′) then reduces to the traditional linear demand faced by duopolists in the homogeneous-product case. Note that here products are horizontally differentiated, whereby the characteristics of a differentiated product appeal to a certain customer base with particular taste.8 In the case above, by inverting (3.1′), one gets the demand function9 qi ( pi , pj ) =
a (1 − s ) − pi + spj . b (1 − s 2 )
(3.7)
Firms select their prices pi , pj (≥ 0 ) simultaneously not knowing the rival’s price decision. The Nash equilibrium prices under Bertrand competition ( piB , pBj ) are such that each firm’s price choice is the best response to the other’s optimal price decision (see related box 3.4): ⎧ π i ( piB , pBj ) ≥ π i ( pi , pBj ) , ∀pi ≥ 0, ⎨ B B B ⎩π j ( pi , pj ) ≥ π j ( pi , pj ) , ∀pj ≥ 0. In the continuous case the first-order profit maximizing condition for a Nash equilibrium is a(1 − s) − 2 pi + spj + c ∂π i = 0. ( pi , pj ) = b (1 − s 2 ) ∂pi Firm i’s best-reply or reaction function is piB ( pj ) =
a (1 − s ) + c s + pj. 2 2
(3.8)
Solving the system of two equations (first-order conditions for firms i and j) with two unknowns ( pi and pj ) results in the following equilibrium prices and quantities: 1− s ⎧ B B B ⎪⎪ p = pi = pj = c + 2 − s (a − c ) ( > c ) , ⎨ a−c ⎪ q B = qiB = q Bj = . b(1 + s)(2 − s) ⎪⎩
(3.9)
8. In this case there are no commonly agreed best features for the product, just individual preferences. Products are not qualitatively ranked on a scale where customers desire the top-quality products but cannot afford them due to budget constraints. This category of differentiated products (Ferrari vs. a standard car) relates to vertical differentiation. Horizontal differentiation refers to situations where customers have taste preferences among a pool of products with comparable quality standards. 9. The resulting profit function is (twice) differentiable and concave, as can be seen from ∂ 2π i 2 =− < 0. ∂pi 2 b (1 − s 2 )
Box 3.4 Solution concepts and Nash equilibrium
Once model assumptions are laid out, one can solve a game-theoretic model using a so-called solution concept.a A solution concept is a methodology for predicting players’ behavior intended to determine the decisions (actions or strategies) that maximize each player’s payoff.b The rationality of each player is typically accepted as a common knowledge; namely each player is aware of the rationality of the other players and acts accordingly.c To obtain stronger predictions about a game outcome, one may impose assumptions beyond common knowledge of rationality.d Here equilibrium strategy profiles must form a Nash equilibrium. Consider n players. Let a i (∈ Ai ) denote firm i ’s pure strategic action (and Ai its strategy set). By convention, a −i represents the strategies played by all other players except i . When firm i chooses strategy a i and her rivals a −i , firm i receives payoff p i (a i , a − i ). A Nash equilibrium is a set of decisions—strategy profile—such that no player can do better by unilaterally changing their decision. A strategy profile (a i*,a − i*) forms a Nash equilibrium if, for any player i, i = 1, …, n,e p i (a i*, a − i*) ≥ p i (a i , a − i*)
∀a i ∈ Ai .
Equivalently, each firm i, i = 1, . . ., n, is faced with the following profitoptimization problem: max p (a i , a − i*). a i ∈Ai
We can interpret the above in terms of best-reply or reaction functions. Let R(a − i ) denote firm i ’s best response to her rivals’ strategies a −i . In Nash equilibrium each player formulates her best response to the other players’ optimal decision; that is, a i * ∈ R(a − i*). Depending on the game specification, such as the order of the play or the available information, the solution concept used may be more stringent as discussed in chapter 4. a. We do not intend here to provide a mathematical account of game theory. Readers should refer to the readings suggested at the end of this chapter for a more formal discussion of game theory. b. We are being vague on the optimality notion. While in problems involving a single decision maker optimality has an unambiguous meaning, in multiplayer decision settings “optimality” is not a well-defined concept. Its understanding is closely related to the solution concept chosen. c. The rationality assumption imposes an important limitation in situations where pride and irrationality play a role. However, building up a reputation as being irrational or unpredictable can also make strategic sense: One can turn such reputation to its advantage by altering opponents’ beliefs. Reagan’s foreign policy toward the Soviet Union might be interpreted in this light. Similarly a firm that has a reputation to wage war with any contestant imposes a credible threat on potential entrants who might think twice before entering. d. Rationalizability, introduced by Bernheim (1984) and Pearce (1984), is considered a fundamental solution concept. Based on the weak assumption that payoffs and players’ rationality are common knowledge, it predicts that a player will not play a strategy that is not a best response to some beliefs about her opponents’ strategies. By iteration, we can narrow down the set of strategies that could be reasonably played (so-called rationalizable strategies). By design, rationalizability makes very weak predictions: the set of rationalizable strategies can be large since it contains all strategy profiles that cannot be excluded based on the assumption of common knowledge of rationality. Nash’s (1950b) equilibrium concept is usually preferred for being more restrictive. e. We consider, here, pure strategies. An equivalent definition exists for mixed strategies, namely convex combinations of pure strategies. By employing mixed strategies, one ensures that (at least) one Nash equilibrium exists (Nash 1950b).
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Thus, in differentiated Bertrand–Nash equilibrium firms set a price, pB, strictly higher than the marginal cost of production, c, earning excess profits. This contrasts to the previous standard Bertrand case involving perfect substitutes (homogeneous products). Moreover, in equilibrium, prices for both differentiated products are equal ( piB = pBj ); this results from the fact that products are horizontally differentiated and that there is (by assumption) no quality premium for one of the products over the other. Finally, the equilibrium price decreases with the degree of substitutability s (∂pB ∂s = − (a − c ) ( 2 − s ) < 0). From (3.9) firm profits are
π B = π iB = π Bj =
1− s ⎛ a − c⎞ ⎜ ⎟ b (1 + s ) ⎝ 2 − s ⎠
2
(> 0) .
(3.10)
Thus, for products that are close to being perfect substitutes (s → 1), the excess profits for the duopolists become zero, a result equivalent to the outcome obtained in the standard Bertrand price competition case. Clearly, firms have an incentive to differentiate their product offerings to soften price competition, departing from the undesirable Bertrand paradox where no firm makes excess profits. This is in line with most marketing and strategic management practices.10 Example 3.2 Differentiated Price Competition Assume the linear market demand function of equation (3.1′) with parameters a = 24, b = 2 3, s = 1 2 and constant variable production cost c = 3. From (3.7) the demand functions are given by ⎧ qi ( pi , pj ) = 24 − 2 pi + pj , ⎨ ⎩q j ( pi , pj ) = 24 − 2 pj + pi . From equation (3.8), firm i’s reaction function is piB ( pj ) = 7.5 +
1 p j. 4
Firm j’s reaction function is obtained symmetrically. In (differentiated) Bertrand price competition, reaction functions are upward-sloping. In Nash equilibrium, equilibrium prices, quantities, and profits, obtained from equations (3.9) and (3.10), are pB = 10, q B = 14, and π B = 98, respectively. This situation is illustrated in figure 3.4. 10. For an economic discussion of the advisable degree of differentiation, refer to Tirole (1988, pp. 286–87).
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Price by firm j ( pj ) 20
Firm i’s reaction function
piB(pj )
15
10
E
Firm j’s reaction function
pjB( pi)
5
0 0
5
10
15
20
Price by firm i (pi) Figure 3.4 Upward-sloping reaction functions in differentiated Bertrand competition a = 24, b = 2 3, s = 1 2 , and c = 3
The differentiated Bertrand model above is fairly descriptive of many real-world rivalry situations in the short term. This model underscores that competition is not just about price but that additional parameters may come into play, such as heterogeneous tastes among customers. The main alternative to price competition is quantity competition represented by the Cournot model.11 Although Bertrand price competition seems more descriptive of certain real-world situations in the short run, Kreps and Scheinkman (1983) suggest that the implicit assumption of a “market auctioneer” in the Cournot model is not necessary. Cournot 11. In Cournot quantity competition, producers simultaneously and independently make production quantity decisions. The output is brought to the market and a “market auctioneer” sets the market-clearing price. In (differentiated) Bertrand price competition, producers simultaneously and independently set prices and there is no need for a price-setting party.
92
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outcomes may also result from a two-stage competition game where duopolists first make a capacity choice, and once these capacity choices become common information, firms set prices as in Bertrand price competition under capacity constraints.12 The two-stage model of Kreps and Scheinkman (1983) results in Cournot-like outcomes and may be more realistic than strict Bertrand price competition in situations involving long-term investments and constrained capacities. For this reason capacity (quantity) is often thought of as a long-term strategic variable, while price is a short-run, tactical action variable. Most of the models discussed in subsequent chapters deal with long-horizon investment problems and therefore build upon the Cournot quantity setting. 3.2.2
Cournot Quantity Competition
Cournot’s (1838) classic quantity competition model characterizes industries in which firms set production schedules in advance and cannot alter them in the short run. The price-setting process is driven by the capacity or quantity chosen by the active firms in the industry.13 Firms produce a homogeneous good. We discuss next two variants of the Cournot duopoly model. We consider first the situation where firms have symmetric costs, and then relax the symmetric cost assumption. A comparison of these two models helps illustrate why firms have an incentive to be cost leader in quantity competition involving a homogeneous good. Cost Symmetry Consider two identical firms (i and j) that face the same, constant unit variable production cost c (≥ 0 ). The linear (inverse) demand function, driven by the total quantity supplied in the market Q ( = qi + q j ), is given by p(Q) = a − bQ,
(3.1)
with a > c and b > 0. Firms’ strategies consist in selecting an output that maximizes profit.14 Equivalently, one can assume that they maximize net 12. From this perspective we can consider the (equilibrium) profit functions in Cournot competition as reduced-form profit functions in which later price competition has been subsumed. Kreps and Scheinkman (1983) show this property in the case where firms face a concave demand function and cannot satisfy all demand according to the “efficientrationing rule.” Investment in new capacity units must be costly for this result to hold. 13. The ex ante chosen production schedules are hard to reverse, so firms cannot influence the market-clearing price-setting process in the short-run. 14. An important assumption of the Cournot model is that firms invest simultaneously and therefore do not observe the strategy chosen by their rivals. This is a case of complete information in a simultaneous game. The appropriate solution concept is the “simple” Nash equilibrium.
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present value (NPV) where value is given by the perpetuity of profits (π k , where k is the risk-adjusted discount rate).15 Firm i’s profit, resulting from the action profile ( qi , q j ), is given by16 p i ( qi , q j ) = [ p (Q) − c ] qi.
(3.11)
As in the monopoly case there is a trade-off between increasing the quantity supplied (qi) and receiving a lower price (and profit margin) because of the upsurge in total industry quantity. This time, however, the firm’s own output choice is not the only factor influencing the pricesetting process; the rival influences it as well. The Nash equilibrium outputs ( qiC , qCj ) are such that each firm’s quantity choice is the best response to the other’s optimal quantity decision. Taking the rival’s quantity choice as given, the first-order condition for firm i’s profit maximization (∂π i ∂qi = 0) yields a − 2bqiC − bq j = c,
(3.12)
or qiC (q j ) =
1⎛a−c − q j ⎞⎟ . ⎜ ⎠ 2⎝ b
(3.13)
Firm j’s first-order profit maximizing condition is obtained symmetrically. If one of the firms does not produce (i.e., if q j = 0), the sole active firm i will supply in equilibrium QM = (a − c ) 2b, as in monopoly. In quantity competition, the reaction functions of duopolists given by (3.13) are downward sloping (i.e., decreasing in rival’s capacity-setting action), as depicted in figure 3.5. The Nash equilibrium outputs can be determined by solving the system of the two equations (3.12) (for firms i and j) with two unknowns (qiC and qCj ) or by substituting firm i’s reaction function from (3.13) into firm j’s (and reciprocally). The equilibrium is found at the intersection (point E) of the two reaction functions: qiC ≡ qiC (qCj ( qiC )) =
1⎛a−c 1⎛a−c − ⎜ − qiC ⎞⎟ ⎞⎟ . ⎜ ⎠⎠ 2⎝ b 2⎝ b
Since symmetric firms presumably produce the same output, the Cournot–Nash equilibrium output by each firm is 15. We assume that firms settle in steady state and that there is no (expected) growth for the underlying profit flow π . 16. The profit function π i (⋅, ⋅) is concave and twice continuously differentiable in qi.
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Quantity supplied by firm j (qj)
( 0; (a−c )/ b)
Firm i’s reaction function
( 0; ( a−c ) / 2b) (qiC ; qjC ) E Firm j’s reaction function
(( a−c ) / 2b; 0 )
(( a−c )/ b; 0 ) Quantity supplied by firm i (qi)
Figure 3.5 Downward-sloping reaction functions in (symmetric) Cournot quantity competition
qC = qiC = qCj =
a−c . 3b
(3.14)
The total industry output then is QC = qiC + qCj =
2 ⎛ a − c⎞ ⎜ ⎟. 3⎝ b ⎠
(3.15)
Two observations are of particular interest. First, in Cournot duopoly each individual firm produces less than does a monopolist firm: qC ≤ QM
⎛ 1 a − c ≤ 1 a − c⎞. ⎜⎝ ⎟ 3 b 2 b ⎠
Second, firms collectively produce more than does a monopolist: QC ≥ QM
⎛ 2 a − c ≥ 1 a − c⎞ . ⎜⎝ ⎟ 3 b 2 b ⎠
Consequently, given the downward-sloping demand (∂p ∂Q < 0), the market-clearing price in Cournot duopoly is lower than in monopoly.
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One can readily deduce the equilibrium price and profit. The equilibrium market-clearing price in Cournot duopoly, pC, obtained by substituting the equilibrium quantity of equation (3.15) into demand equation (3.1), is pC = c +
a−c 3
(> c ) .
(3.16)
The equilibrium profit for firm i ( j) is
π C = π iC = π Cj =
(a − c )2 9b
.
(3.17)
Due to the lower individual output and lower market-clearing price, a Cournot duopolist earns lower profits than a monopolist (π iC ≤ π iM ). From a social-welfare viewpoint, Cournot duopoly is better than monopoly as the total quantity supplied is higher, the equilibrium marketclearing price lower, and the profit or producer surplus lower.17 What would happen if the duopolists could collaborate to improve their joint profits? Suppose that firms could (tacitly) collude, choosing to maximize their joint profit instead of their individual profits. In this case the symmetric duopolists would select the optimal cumulated quantity, Q, and then divide up the higher joint-profit pie. The optimal total industry output selected by the cartel would be the monopoly quantity, QM. Each firm could, for example, produce half of it and earn half of the monopoly rent. Since π M 2 ≥ π iC, a collusive agreement seems preferable at first sight. This strategy profile is not stable, however. The contract ruling the cartel is not self-enforceable because each firm has an incentive to cheat, increasing the quantity it supplies to benefit from a higher price.18 Firms would increase their output until the Cournot–Nash equilibrium output ((a − c ) 3b ; (a − c ) 3b) is reached. This is the only stable equilibrium in this simultaneous quantity game. Collusion is not likely 17. This efficiency result is based on the premise that fixed costs are immaterial. The presence of material fixed costs might substantially alter this result, since a natural monopoly might turn out to be socially optimal. 18. To demonstrate the instability of the collaborative outcome, consider first the optimal reaction of the deviating party (e.g., firm i). Substituting half the monopoly quantity given in (3.2) into firm i’s reaction function in (3.13), the best reply obtains qiC (Q M 2 ) = 3 (a − c ) 8b . The resulting market-clearing price is p = c + 3 (a − c ) 8 . The profit for the deviating party is higher than half the monopoly profit, creating an incentive to deviate from the cartel agreement:
πD =
9 πM ⎛ πM ⎞ ⎜> ⎟. 8 2 ⎝ 2 ⎠
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to occur since each firm has an incentive to deviate, being pulled as in a prisoner’s dilemma to the inferior Nash equilibrium outcome. Collusion will thus not occur in such a static one-shot duopoly game. This ultimately benefits consumers who can purchase the product at a lower price.19 Cost Asymmetry Suppose now that the two firms have different marginal costs ci ≠ c j, with firm i’s cost function given by Ci (qi ) = ci qi , ci ≥ 0. If ci < c j , firm i produces the (homogeneous) good more efficiently than firm j, enjoying a cost-leader advantage. Assuming the same (inverse) demand function as before, firm i’s profit function is p i (Q) = ( p (Q) − ci ) qi. Due to strategic interaction, firm i’s quantity choice ultimately depends on its own cost ci as well as on its rival’s cost c j. The cost differential, c j − ci (≠ 0 ), does matter. From the first-order profit-maximizing condition, firm i’s reaction function is qiC (q j ) =
1 ⎛ a − ci − q j ⎞⎟ . ⎜ ⎠ 2⎝ b
(3.18)
Substituting the reaction functions (into each other), firm i’s equilibrium quantity is20 qiC =
a − 2ci + c j . 3b
(3.19)
In equilibrium, firm i produces more when its rival is cost disadvantaged or the latter’s marginal production cost is increased (∂qiC ∂c j > 0). The resulting industry equilibrium price is pC =
a + ci + c j . 3
(3.20)
Firm i’s resulting equilibrium profit is
π = C i
(a − 2ci + c j )2 9b
.
(3.21)
Useful insights can be deduced from the latter expression. The cost leader (here firm i) earns higher profits in the market than its less efficient rival. This provides an economic rationale for the cost leadership strategy proposed by Porter (1980). As the firm’s profit decreases in its 19. We discuss later how (tacit) collusion can sustain itself as stable industry equilibrium in certain repeated games. 20. The formulas for firm j are analogous, meaning one can obtain firm j’s quantity by substituting j for i and i for j in the expression above.
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97
own cost (∂π iC ∂ci < 0), the cost leader would further benefit from an improved cost position. An alternative, indirect tactic to achieve cost leadership is to increase the rival’s cost (∂π iC ∂c j > 0), for example, by limiting its access to scarce resources it needs. 3.2.3
Strategic Substitutes versus Complements
As noted earlier, reaction functions may be downward sloping (as in the Cournot quantity competition model) or upward sloping (as in differentiated Bertrand price competition). This distinction between these two kinds of reactions is at the core of the notion of strategic substitutes versus strategic complements introduced by Bulow, Geanakoplos, and Klemperer (1985). The notions of strategic substitutes and complements are meant to capture how competitors are expected to react when a firm changes its strategic decision variables, such as price or quantity. This distinction is informative on whether a firm reacts to its rivals’ strategic actions in a reciprocating or in a contrarian way. The notion of commitment—analyzed extensively in industrial organization—rests on these types of reaction functions. When reaction functions are downward sloping, the actions are strategic substitutes. Let α i ∈ Ai be firm i’s strategic action variable (with Ai its action set) and Ri (α j ) its best reply to rival action αj.21 The general form of the equilibrium in case of strategic substitutes is depicted in figure 3.6. In figure 3.6a, a particular case of interest is the reaction function in Cournot quantity competition. Assuming a linear (inverse) demand function as in equation (3.1) and a linear cost function, Ci (qi ) = cqi with c constant, firm i’s reaction function is obtained in (3.13) as qiC (q j ) =
a−c 1 − qj. 2b 2
Since ∂qiC ∂q j < 0, if a Cournot duopolist increases its output, its rival will do the opposite by reducing it, a contrarian reaction. Cournot quantity competition is a game where actions are strategic substitutes. In case of strategic complements, competitive reactions are similar or reciprocating. The distinction again relates to the shape (slope) of the reaction function. When reaction functions are upward sloping, as in figure 3.6b, the firms’ actions are strategic complements or reciprocal. In the (differentiated) Bertrand model, we deal with strategic complements 21. We assume that the firms’ reaction functions are characterized by slopes of the same sign, meaning both are downward sloping or both upward sloping.
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Chapter 3
aj
R i (a j) a j*
E
R j (a i )
a i*
ai
(a)
aj
R i (a j)
a j*
E'
R j (a i )
a i*
ai
(b) Figure 3.6 Downward and upward-sloping reaction functions compared: strategic substitutes versus complements (a) Strategic substitutes or contrarian reactions ( ∂Ri ∂α j < 0 ); (b) strategic complements or reciprocating reactions ( ∂Ri ∂α j > 0 )
since a reduction in price by a firm is an optimal response to its competitor’s price cut. If the inverse demand function is linear as in equation (3.1′), firm i’s reaction function is given by (3.8) above, namely piB ( pj ) =
a (1 − s ) + c s + pj . 2 2
Since ∂piB ∂pj ≥ 0, when a Bertrand duopolist decreases its price, its rival will follow suit and decrease its price as well, a reciprocating reaction. For perfect substitutes (s → 1), such reciprocating reactions can lead
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to the Bertrand paradox, where firms end up waging a fierce price war and make zero economic profits (as in perfect competition). Equilibrium outcomes in case of strategic substitutes vs. complements are illustrated as outcomes E and E ′ in figure 3.6. The slope of the reaction function matters. Whether actions are strategic substitutes or complements can greatly affect the optimal decisionmaking in such settings. Following Gal-Or (1985), when reaction functions are downward sloping (involving strategic substitutes or contrarian reactions), each firm has an incentive to take the lead, acting as a Stackelberg leader and enjoying a first-mover advantage. This can lead, however, to situations involving preemption as firms strive to seize this advantage ahead of competitors. By contrast, in case of strategic complements or reciprocating actions, the Stackelberg follower is better off, benefiting from a second-mover advantage. For instance, under price competition no one has an incentive to set its price first if the follower can undercut it later.22 This kind of strategic interaction could result in a war of attrition where firms compete to be the last to make a move, each firm doing its utmost not to be considered a coward (chicken game). 3.3
Oligopoly and Perfect Quantity Competition
In the previous sections we analyzed industry structure where only two firms are incumbent. Although duopoly situations exist (e.g., the competition between Boeing and Airbus in the aircraft manufacturing industry), there is often more than two players operating in a given industry. Numerous examples of such industries abound. A case in point is the automotive sector, with giants such as Volkswagen (including Audi, Porsche, and Seat), Toyota, Ford, BMW, Daimler, and GM. The economic analysis of such oligopolistic structures can provide interesting insights as well. The basic quantity duopoly setup analyzed by Cournot (1838) may be refined to consider more than two competitors. At first we analyze below an oligopoly with cost asymmetry. Subsequently we discuss the simpler case of oligopoly under cost symmetry (as a special case). We confirm that as the number of competitors substantially increases, the Cournot oligopoly outcome tends toward the perfect competition benchmark. This analysis is useful later in the analysis of option games in both discrete and continuous time. 22. A firm is better off to defer its decision and select its own price until after having full knowledge of the price charged by the leader.
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Assume n oligopolist firms competing in quantity over a homogeneous product. Each firm i faces a linear demand function as in equation (3.1) and marginal production cost ci. Without loss of generality, firms are ranked in cost advantage with firm 1 having the largest cost advantage or lowest cost c1. Firm i’s profit function is given by
π i (qi , Q− i ) = [ p (qi , Q− i ) − ci ] qi ,
i = 1, . . ., n,
where Q− i stands for the quantity produced collectively by all other suppliers except firm i, with Q = qi + Q− i . To obtain the Cournot–Nash equilibrium, one has to deduce the optimal production strategy profile (q1C , . . ., qnC ) such that each firm i, i = 1, . . ., n, maximizes profit p i ( qi , Q−Ci ) , considering other rivals’ choices as given. This leads to the first-order condition: MRi ( qiC , Q−Ci ) = qiC ×
∂p C C (qi , Q− i ) + p (qiC , Q−Ci ) = ci , ∂qi
i = 1, . . ., n. (3.22)
In the linear demand case, marginal revenue is MRi (qi , Q− i ) = a − 2bqi − bQ− i . In the general Cournot oligopoly with n asymmetric firms, we must solve the following system of n equations with n unknowns: ⎧ a − 2bq1C − b (QC − q1C ) = c1 , ⎪ ⎪⎪ C C C ⎨ a − 2bqi − b (Q − qi ) = ci , ⎪ ⎪ C ⎪⎩a − 2bqn − b (QC − qnC ) = cn . Summation leads to a total quantity for the entire market of n ⎞⎛a−c⎞ QC (n) = ⎛⎜ , ⎝ n + 1⎟⎠ ⎜⎝ b ⎟⎠
(3.23)
where c ≡ ∑ j = 1 c j n is the average (variable) production cost in the industry. Denote by c− i ≡ ∑ j ≠ i c j (n − 1) the average production cost for all other firms except i. Individual firm quantities are obtained by substituting the total industry quantity of (3.23) into each equation of the system above: n
Market Structure Games: Static
qiC (n) =
101
1 ⎛ a − nci + ( n − 1) c− i ⎞ ⎜ ⎟⎠ , n + 1⎝ b
i = 1, . . ., n .
(3.24)
The equilibrium price obtains from (3.1) and (3.23) as pC ( n) = c +
a−c n+1
(> c1 ).
(3.25)
Regardless of how many firms operate in the market, the most costadvantaged firm (absolute cost leader) always enjoys a price strictly higher than its variable cost c1.23 The resulting profit for firm i is p iC ( n) =
(a − nci + (n − 1) c− i )2
1
(n + 1)2
b
,
i = 1, . . ., n .
(3.26)
It can be seen that profits are increasing in the cost advantage. Moreover, a firm’s profitability depends on its production cost, its market share, and the price elasticity of demand.24 The special case of cost symmetry (with ci = c) has clear-cut insights. The equilibrium quantity, obtained from equation (3.24), is the same for each symmetric firm: qC ( n) =
1 ⎛ a − c⎞ ⎜ ⎟, n + 1⎝ b ⎠
i = 1, . . ., n.
(3.27)
The equilibrium price, resulting from equation (3.25), is given by pC (n) = c +
a−c n+1
( > c ).
(3.28)
The expression above suggests that even for a large oligopoly (n large), the equilibrium price margin, pC − c, remains positive. From (3.26), the profit for each identical oligopolist firm is
π C (n) =
1
(n + 1)2
( a − c )2 b
,
i = 1, . . ., n.
(3.29)
23. This holds as long as the demand intercept exceeds the average unit cost in the industry. 24. Let si ≡ qiC QC denote firm i’s (equilibrium) market share and recall the price elasticity of demand e p in equation (3.5). The first-order condition in (3.22) yields firm i’s Lerner index: Li ≡ ( pC − ci ) pC = − si ε p . This measure proxies for firm i’s profit margin and depends on ci , si , and ε p .
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Chapter 3
In the special case when there is only one firm operating (n = 1), the previous monopoly results in equations (3.2) to (3.4) obtain. In duopoly (n = 2), we obtain the symmetric Cournot duopoly results of equations (3.14) to (3.17). As the number of active firms is greatly increased (n → ∞), the quantity produced by any one firm becomes negligible, the market-clearing price approaches marginal production cost, c, and the economic profit of an individual firm approaches zero. These outcomes confirm the well-known results in perfect competition: firms make no excess profits and set prices at marginal cost. The presence of fixed production or investment costs may affect the number of firms operating in the sector. 3.4
Market Structure under Incomplete Information
The standard Cournot model assumes that duopolists know perfectly the cost structure of their rivals; their profit-maximizing quantities are derived as a function of the known variable costs. This assumption might not hold in certain industry settings characterized by information asymmetry. Suppose that firm j does not know for sure firm i’s cost but that “nature” will reveal it with certain probability. For example, firm i may have invested in a new innovative process that could alter its cost structure if its R&D efforts succeed (with probability P). The rival’s cost can be either low (cL) with probability P or high (cH ) with probability 1 − P (cL < cH).25 That is, firm j has probabilistic beliefs about its rival’s type (cost). Suppose also that firm i can perfectly observe what “nature” decided concerning its own cost (e.g., the outcome of its own R&D efforts), as well as its rival’s cost (e.g., the rival uses the prevalent production technology). Firm j is aware of the information asymmetry in favor of firm i. Once firm i knows its own cost, the two firms choose simultaneously their capacity or output. Figure 3.7 depicts this situation. In selecting its optimal quantity, firm j no longer maximizes its deterministic profit function but instead maximizes its expected profit function, taking into account the uncertainty it faces concerning firm i’s type or cost. Firm j knows that if the low-cost outcome (cL) occurs (with probability P), firm i’s reaction function would be similar to equation (3.18) in the Cournot game: qiC ( q j , cL ) =
1 ⎛ a − cL − q j ⎞⎟ . ⎜⎝ ⎠ 2 b
25. Subscript L stands for low- and H for high-cost type.
(3.30)
Market Structure Games: Static
103
Nature
cL (low cost)
Firm i qi (cL ) = qL
cH (high cost)
Firm i qi (cH) = qH
Firm j
qj
Firm j
qj
Figure 3.7 Extensive form of Cournot competition under asymmetric information The dashed box indicates that firm j has two nodes in its information set and is unable to anticipate its rival’s quantity decision.
However, in case the high-cost outcome (cH ) occurs (with probability 1 − P), firm i’s reaction function is instead qiC ( q j , cH ) =
1 ⎛ a − cH − q j ⎞⎟ . ⎜ ⎠ 2⎝ b
(3.31)
Firm j’s expected profit reflects its belief (probabilistic distribution) about firm i’s possible production costs. It is given by26
π j ( q j , qL , qH ) ≡ E [π j (qi , qL , qH )] = P × π j ( q j , qL ) + (1 − P ) × π j ( q j , qH ) ,
(3.32)
where qL and qH stand for firm i’s quantity choice as a function of its low or high marginal cost. Firm j’s (Bayesian Nash) equilibrium strategy consists in selecting its output q j so as to maximize its above expected profit in (3.32), considering its rival’s optimal decision as given. From the first-order profit optimization condition (and substitution of equations 26. Firm j ’s profit function is differentiable and concave in its own strategic action q j ( ∂ 2π j ∂q j 2 < 0 since b > 0). The first-order condition is both
