Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Interaction [NOOK Book]

Overview

Since its original publication in 2000, Game Theory Evolving has been considered the best textbook on evolutionary game theory. This completely revised and updated second edition of Game Theory Evolving contains new material and shows students how to apply game theory to model human behavior in ways that reflect the special nature of sociality and individuality. The textbook continues its in-depth look at cooperation in teams, agent-based simulations, experimental economics, the evolution and diffusion of ...

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Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Interaction

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Overview

Since its original publication in 2000, Game Theory Evolving has been considered the best textbook on evolutionary game theory. This completely revised and updated second edition of Game Theory Evolving contains new material and shows students how to apply game theory to model human behavior in ways that reflect the special nature of sociality and individuality. The textbook continues its in-depth look at cooperation in teams, agent-based simulations, experimental economics, the evolution and diffusion of preferences, and the connection between biology and economics.

Recognizing that students learn by doing, the textbook introduces principles through practice. Herbert Gintis exposes students to the techniques and applications of game theory through a wealth of sophisticated and surprisingly fun-to-solve problems involving human and animal behavior. The second edition includes solutions to the problems presented and information related to agent-based modeling. In addition, the textbook incorporates instruction in using mathematical software to solve complex problems. Game Theory Evolving is perfect for graduate and upper-level undergraduate economics students, and is a terrific introduction for ambitious do-it-yourselfers throughout the behavioral sciences.

  • Revised and updated edition relevant for courses across disciplines
  • Perfect for graduate and upper-level undergraduate economics courses
  • Solutions to problems presented throughout
  • Incorporates instruction in using computational software for complex problem solving
  • Includes in-depth discussions of agent-based modeling
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Editorial Reviews

From the Publisher
"Gintis has wholeheartedly embraced the evolutionary approach to games. . .The author is an accomplished economist raised in the classical mold, and his background shown in many aspects of the book . . . He himself has important things to say . . . ."—Karl Sigmund, Science
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Product Details

  • ISBN-13: 9781400830077
  • Publisher: Princeton University Press
  • Publication date: 1/26/2009
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition description: Second
  • Edition number: 2
  • Pages: 408
  • Sales rank: 1,259,224
  • File size: 11 MB
  • Note: This product may take a few minutes to download.

Meet the Author

Herbert Gintis holds faculty positions at the Santa Fe Institute, Central European University, and University of Siena. He has coedited numerous books, including "Moral Sentiments and Material Interests, Unequal Chances" (Princeton), and "Foundations of Human Sociality".
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Table of Contents

Preface xv

Chapter 1: Probability Theory 1
1.1 Basic Set Theory and Mathematical Notation 1
1.2 Probability Spaces 2
1.3 De Morgan's Laws 3
1.4 Interocitors 3
1.5 The Direct Evaluation of Probabilities 3
1.6 Probability as Frequency 4
1.7 Craps 5
1.8 A Marksman Contest 5
1.9 Sampling 5
1.10 Aces Up 6
1.11 Permutations 6
1.12 Combinations and Sampling 7
1.13 Mechanical Defects 7
1.14 Mass Defection 7
1.15 House Rules 7
1.16 The Addition Rule for Probabilities 8
1.17 A Guessing Game 8
1.18 North Island, South Island 8
1.19 Conditional Probability 9
1.20 Bayes' Rule 9
1.21 Extrasensory Perception 10
1.22 Les Cinq Tiroirs 10
1.23 Drug Testing 10
1.24 Color Blindness 11
1.25 Urns 11
1.26 The Monty Hall Game 11
1.27 The Logic of Murder and Abuse 11
1.28 The Principle of Insufficient Reason 12
1.29 The Greens and the Blacks 12
1.30 The Brain and Kidney Problem 12
1.31 The Value of Eyewitness Testimony 13
1.32 When Weakness Is Strength 13
1.33 The Uniform Distribution 16
1.34 Laplace's Law of Succession 17
1.35 From Uniform to Exponential 17

Chapter 2: Bayesian Decision Theory 18
2.1 The Rational Actor Model 18
2.2 Time Consistency and Exponential Discounting 20
2.3 The Expected Utility Principle 22
2.4 Risk and the Shape of the Utility Function 26
2.5 The Scientific Status of the Rational Actor Model 30

Chapter 3: Game Theory: Basic Concepts 32
3.1 Big John and Little John 32
3.2 The Extensive Form 38
3.3 The Normal Form 41
3.4 Mixed Strategies 42
3.5 Nash Equilibrium 43
3.6 The Fundamental Theorem of Game Theory 44
3.7 Solving for Mixed-Strategy Nash Equilibria 44
3.8 Throwing Fingers 46
3.9 Battle of the Sexes 46
3.10 The Hawk-Dove Game 48
3.11 The Prisoner's Dilemma 50

Chapter 4: Eliminating Dominated Strategies 52
4.1 Dominated Strategies 52
4.2 Backward Induction 54
4.3 Exercises in Eliminating Dominated Strategies 55
4.4 Subgame Perfection 57
4.5 Stackelberg Leadership 59
4.6 The Second-Price Auction 59
4.7 The Mystery of Kidnapping 60
4.8 The Eviction Notice 62
4.9 Hagar's Battles 62
4.10 Military Strategy 63
4.11 The Dr. Strangelove Game 64
4.12 Strategic Voting 64
4.13 Nuisance Suits 65
4.14 An Armaments Game 67
4.15 Football Strategy 67
4.16 Poker with Bluffing 68
4.17 The Little Miss Muffet Game 69
4.18 Cooperation with Overlapping Generations 70
4.19 Dominance-Solvable Games 71
4.20 Agent-based Modeling 72
4.21 Why Play a Nash Equilibrium? 75
4.22 Modeling the Finitely-Repeated Prisoner's Dilemma 77
4.23 Review of Basic Concepts 79

Chapter 5: Pure-Strategy Nash Equilibria 80
5.1 Price Matching as Tacit Collusion 80
5.2 Competition on Main Street 81
5.3 Markets as Disciplining Devices: Allied Widgets 81
5.4 The Tobacco Market 87
5.5 The Klingons and the Snarks 87
5.6 Chess: The Trivial Pastime 88
5.7 No-Draw, High-Low Poker 89
5.8 An Agent-based Model of No-Draw, High-Low Poker 91
5.9 The Truth Game 92
5.10 The Rubinstein Bargaining Model 94
5.11 Bargaining with Heterogeneous Impatience 96
5.12 Bargaining with One Outside Option 97
5.13 Bargaining with Dual Outside Options 98
5.14 Huey, Dewey, and Louie Split a Dollar 102
5.15 Twin Sisters 104
5.16 The Samaritan's Dilemma 104
5.17 The Rotten Kid Theorem 106
5.18 The Shopper and the Fish Merchant 107
5.19 Pure Coordination Games 109
5.20 Pick Any Number 109
5.21 Pure Coordination Games: Experimental Evidence 110
5.22 Introductory Offers 111
5.23 Web Sites (for Spiders) 112

Chapter 6: Mixed-Strategy Nash Equilibria 116
6.1 The Algebra of Mixed Strategies 116
6.2 Lions and Antelope 117
6.3 A Patent Race 118
6.4 Tennis Strategy 119
6.5 Preservation of Ecology Game 119
6.6 Hard Love 120
6.7 Advertising Game 120
6.8 Robin Hood and Little John 122
6.9 The Motorist's Dilemma 122
6.10 Family Politics 123
6.11 Frankie and Johnny 123
6.12 A Card Game 124
6.13 Cheater-Inspector 126
6.14 The Vindication of the Hawk 126
6.15 Characterizing 2 x 2 Normal Form Games I 127
6.16 Big John and Little John Revisited 128
6.17 Dominance Revisited 128
6.18 Competition on Main Street Revisited 128
6.19 Twin Sisters Revisited 129
6.20 Twin Sisters: An Agent-Based Model 129
6.21 One-Card, Two-Round Poker with Bluffing 131
6.22 An Agent-Based Model of Poker with Bluffing 132
6.23 Trust in Networks 133
6.24 El Farol 134
6.25 Decorated Lizards 135
6.26 Sex Ratios as Nash Equilibria 137
6.27 A Mating Game 140
6.28 Coordination Failure 141
6.29 Colonel Blotto Game 141
6.30 Number Guessing Game 142
6.31 Target Selection 142
6.32 A Reconnaissance Game 142
6.33 Attack on Hidden Object 143
6.34 Two-Person, Zero-Sum Games 143
6.35 Mutual Monitoring in a Partnership 145
6.36 Mutual Monitoring in Teams 145
6.37 Altruism(?) in Bird Flocks 146
6.38 The Groucho Marx Game 147
6.39 Games of Perfect Information 151
6.40 Correlated Equilibria 151
6.41 Territoriality as a Correlated Equilibrium 153
6.42 Haggling at the Bazaar 154
6.43 Poker with Bluffing Revisited 156
6.44 Algorithms for Finding Nash Equilibria 157
6.45 Why Play Mixed Strategies? 160
6.46 Reviewing of Basic Concepts 161

Chapter 7: Principal-AgentModels 162
7.1 Gift Exchange 162
7.2 Contract Monitoring 163
7.3 Profit Signaling 164
7.4 Properties of the Employment Relationship 168
7.5 Peasant and Landlord 169
7.6 Bob's Car Insurance 173
7.7 A Generic Principal-Agent Model 174

Chapter 8: Signaling Games 179
8.1 Signaling as a Coevolutionary Process 179
8.2 A Generic Signaling Game 180
8.3 Sex and Piety: The Darwin-Fisher Model 182
8.4 Biological Signals as Handicaps 187
8.5 The ShepherdsWho Never Cry Wolf 189
8.6 My Brother's Keeper 190
8.7 Honest Signaling among Partial Altruists 193
8.8 Educational Signaling 195
8.9 Education as a Screening Device 197
8.10 Capital as a Signaling Device 199

Chapter 9: Repeated Games 201
9.1 Death and Discount Rates in Repeated Games 202
9.2 Big Fish and Little Fish 202
9.3 Alice and Bob Cooperate 204
9.4 The Strategy of an Oil Cartel 205
9.5 Reputational Equilibrium 205
9.6 Tacit Collusion 206
9.7 The One-Stage Deviation Principle 208
9.8 Tit for Tat 209
9.9 I'd Rather Switch Than Fight 210
9.10 The Folk Theorem 213
9.11 The Folk Theorem and the Nature of Signaling 216
9.12 The Folk Theorem Fails in Large Groups 217
9.13 Contingent Renewal Markets Do Not Clear 219
9.14 Short-Side Power in Contingent Renewal Markets 222
9.15 Money Confers Power in Contingent Renewal Markets 223
9.16 The Economy Is Controlled by the Wealthy 223
9.17 Contingent Renewal Labor Markets 224

Chapter 10: Evolutionarily Stable Strategies 229
10.1 Evolutionarily Stable Strategies: Definition 230
10.2 Properties of Evolutionarily Stable Strategies 232
10.3 Characterizing Evolutionarily Stable Strategies 233
10.4 A Symmetric Coordination Game 236
10.5 A Dynamic Battle of the Sexes 236
10.6 Symmetrical Throwing Fingers 237
10.7 Hawks, Doves, and Bourgeois 238
10.8 Trust in Networks II 238
10.9 Cooperative Fishing 238
10.10 Evolutionarily Stable Strategies Are Not Unbeatable 240
10.11 A Nash Equilibrium That Is Not an EES 240
10.12 Rock, Paper, and Scissors Has No ESS 241
10.13 Invasion of the Pure-Strategy Mutants 241
10.14 Multiple Evolutionarily Stable Strategies 242
10.15 Evolutionarily Stable Strategies in Finite Populations 242
10.16 Evolutionarily Stable Strategies in Asymmetric Games 244

Chapter 11: Dynamical Systems 247
11.1 Dynamical Systems: Definition 247
11.2 Population Growth 248
11.3 Population Growth with Limited Carrying Capacity 249
11.4 The Lotka-Volterra Predator-Prey Model 251
11.5 Dynamical Systems Theory 255
11.6 Existence and Uniqueness 256
11.7 The Linearization Theorem 257
11.8 Dynamical Systems in One Dimension 258
11.9 Dynamical Systems in Two Dimensions 260
11.10 Exercises in Two-Dimensional Linear Systems 264
11.11 Lotka-Volterra with Limited Carrying Capacity 266
11.12 Take No Prisoners 266
11.13 The Hartman-Grobman Theorem 267
11.14 Features of Two-Dimensional Dynamical Systems 268

Chapter 12: Evolutionary Dynamics 270
12.1 The Origins of Evolutionary Dynamics 271
12.2 Strategies as Replicators 272
12.3 A Dynamic Hawk-Dove Game 274
12.4 Sexual Reproduction and the Replicator Dynamic 276
12.5 Properties of the Replicator System 278
12.6 The Replicator Dynamic in Two Dimensions 279
12.7 Dominated Strategies and the Replicator Dynamic 280
12.8 Equilibrium and Stability with a Replicator Dynamic 282
12.9 Evolutionary Stability and Asymptotically Stability 284
12.10 Trust in Networks III 284
12.11 Characterizing 2 x 2 Normal Form Games II 285
12.12 Invasion of the Pure-Strategy Nash Mutants II 286
12.13 A Generalization of Rock, Paper, and Scissors 287
12.14 Uta stansburiana in Motion 287
12.15 The Dynamics of Rock, Paper, and Scissors 288
12.16 The Lotka-VolterraModel and Biodiversity 288
12.17 Asymmetric Evolutionary Games 290
12.18 Asymmetric Evolutionary Games II 295
12.19 The Evolution of Trust and Honesty 295

Chapter 13: Markov Economies and Stochastic Dynamical Systems 297
13.1 Markov Chains 297
13.2 The Ergodic Theorem for Markov Chains 305
13.3 The Infinite Random Walk 307
13.4 The Sisyphean Markov Chain 308
13.5 Andrei Andreyevich's Two-Urn Problem 309
13.6 Solving Linear Recursion Equations 310
13.7 Good Vibrations 311
13.8 Adaptive Learning 312
13.9 The Steady State of a Markov Chain 314
13.10 Adaptive Learning II 315
13.11 Adaptive Learning with Errors 316
13.12 Stochastic Stability 317

Chapter 14: Table of Symbols 319
Chapter 15: Answers 321
Sources for Problems 373
References 375

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