本书是任何从事学习理论和博弈理论研究或在应用研究中使用演进博弈理论的人的必读书籍。不同于非合作博弈理论中传统的均衡概念所认为的均衡是在博弈的规则和参与人的收益函数都共知的情况下，由理性参与人的分析和自省产生的结果，《博弈学习理论》则认为均衡是并非完全理性的参与人随着时间的推移寻求优化这一过程的长期结果。

Series Foreword xi
Acknowledgments xiii
1 Introduction
1.I Introduction
1.2 Large Populations and Matching Models
1.3 Three Common Models of Learning and／or Evolution
1.4 Cournot Adjustment
1.5 Analysis of Cournot Dynamics
1.6 Cournot Process with Lock.In
1.7 Review of Finite Simultaneous—Move Games
Appendix：Dynamical Systems and Local Stability
References
2 Fictitious Play
2.1 Introduction
2.2 Two—Player Fictitious Play
2.3 Asymptotic Behavior of Fictitious Play
2.4 Interpretation of Cycles in Fictitious Play
2.5 Multiplayer Fictitious Play
2.6 Payoffs in Fictitious Play
2.7 Consistency and Correlated Equilibrium in Games with Two Strategies
2.8 Fictitious Play and the Best-Response Dynamic
2.9 Generalizations of Fictitious Play
Appendix：Dirichlet Priors and Multinomial Sampling
References
3 Replicator Dynamics and Related Deterministic Models of Evolution
3.1 Introduction
3.2 Replicator Dynamics in a Homogeneous Population
3.3 Stability in the Homogeneous—Population Replicator Dynamic
3.4 Evolutionarily Stable Strategies
3.5 Asymmetric Replicator Models
3.6 Interpretation of the Replicator Equation
3.7 Generalizations of the Replicator Dynamic and Iterated Strict Dominance
3.8 Myopic Adjustment Dynamics
3.9 Set-Valued Limit Points and Drift
3.10 Cheap Talk and the Secret Handshake
3.11 Discrete.Time Replicator Systems
Appendix：Liouville’S Theorem
References
4 Stochastic Fictitious Play and Mixed—Strategy Equilibria
4.1 Introduction
4.2 Notions of Convergence
4.3 Asymptotic Myopia and Asymptotic Empiricism
4.4 Randomly Perturbed Payoffs and Smoothed Best Responses
4.5 Smooth Fictitious Play and Stochastic Approximation
4.6 PartiaI Sampling
4.7 Universal Consistency and Smooth Fictitious Play
4.8 Stimulus—Response and Fictitious Play as Learning Models
4.9 Learning about Strategy Spaces
Appendix：Stochastic Approximation Theory
References
5 Adiustment Models with Persistent Randomness
5.1 Introduction
5.2 Overview of Stochastic Adjustment Models
5.3 Kandori—Mailath—Rob Model
5.4 Discussion of Other Dynamics
5.5 Local Interaction
5.6 Radius and Coradius of Basins of Attraction
5.7 Modified Coradius
5.8 Uniform Random Matching with Heterogeneous Populations
5.9 Stochastic Replicator Dynamics
Appendix A：Review of Finite Markov Chains
Appendix B：Stochastic Stability Analysis
RefeFences
6 Extensive。Form Games and Self—confirming Equilibrium
6.1 Introduction
6.2 An Example
6.3 Extensive—Form Games
6.4 A Simple Learning Model
6.5 Stability Of Self—confirming Equilibrium
6.6 Heterogeneous Self-confirming Equilibrium
6.7 Consistent Self-confirming Equilibrium
6.8 Consistent Self-confirming Equilibria and Nash Equilibria
6.9 Rationalizable SCE and Prior Information on Opponents’ Payoffs
References
7 Nash Equilibrium，Large Population Models，and Mutations in
Extensive.Form Games
7.I Introduction
7.2 Relevant Information Sets and Nash Equilibrium
7.3 Exogenous Experimentation
7.4 Learning in Games Compared to the Bandit Problem
7.5 Steady—State Learning
7.6 Stochastic Adjustment and Backward Induction in a Model of‘Fast Learning’
7.7 Mutations and Fast Learning in Models of Cheap Talk
7.8 Experimentation and the Length of the Horizon
Appendix：Review of Bandit Problems
References
8 Sophisticated Learning
8.1 Introduction
8.2 Three Paradigms for Conditional Learning
8.3 Bayesian Approach to Sophisticated Learning
8.4 Interpreting the Absolute Continuity Condition
8.5 Choosing among Experts
8.6 Conditional Learning
8.7 Discounting
8.8 Categorization Schemes and Cycles
8.9 Introspective Classification Rules，Calibration，and Correlated Equilibrium
8.10 Sonsino’S Model o