全新正版图书 博弈学/原版影印科技书朱·弗登博格世界图书出版公司9787519264628蔚蓝书店 snb txt 网盘 pdf kindle 2025 lit 下载

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 Modelr/>

1.3 Three Common Models of Learning and／or Evolution

1.4 Cournot Adjustment

1.5 Analysis of Cournot Dynamicr/>

1.6 Cournot Process with Lock.In

1.7 Review of Finite Simultaneous―Move Gamer/>

Appendix：Dynamical Systems and Local Stability

Referencer/>

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 Strategier/>

2.8 Fictitious Play and the Best-Response Dynamic

2.9 Generalizations of Fictitious Play

Appendix：Dirichlet Priors and Multinomial Sampling

Referencer/>

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 Strategier/>

3.5 Asymmetric Replicator Modelr/>

3.6 Interpretation of the Replicator Equation

3.7 Generalizations of the Replicator Dynamic and Iterated Strict Dominance

3.8 Myopic Adjustment Dynamicr/>

3.9 Set-Valued Limit Points and Drift

3.10 Cheap Talk and the Secret Handshake

3.11 Discrete.Time Replicator Systemr/>

Appendix：Liouville’S Theorem

Referencer/>

4 Stochastic Fictitious Play and Mixed―Strategy Equilibria

4.1 Introduction

4.2 Notions of Convergence

4.3 Asymptotic Myopia and Asymptotic Empiricir/>

4.4 Randomly Perturbed Payoffs and Smoothed Best Responser/>

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 Modelr/>

4.9 Learning about Strategy Spacer/>

Appendix：Stochastic Approximation Theory

Referencer/>

5 Adiustment Models with Persistent Randomner/>

5.1 Introduction

5.2 Overview of Stochastic Adjustment Modelr/>

5.3 Kandori―Mailath―Rob Model

5.4 Discussion of Other Dynamicr/>

5.5 Local Interaction

5.6 Radius and Coradius of Basins of Attraction

5.7 Modified Coradiur/>

5.8 Uniform Random Matching with Heterogeneous Populationr/>

5.9 Stochastic Replicator Dynamicr/>

Appendix A：Review of Finite Markov Chainr/>

Appendix B：Stochastic Stability Analysir/>

RefeFencer/>

6 Extensive。Form Games and Self―confirming Equilibrium

6.1 Introduction

6.2 An Example

6.3 Extensive―Form Gamer/>

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’ Payoffr/>

Referencer/>

7 Nash Equilibrium，Large Population Models，and Mutations in

Extensive.Form Gamer/>

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 Problemr/>

Referencer/>

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 Expertr/>

8.6 Conditional Learning

8.7 Discounting

8.8 Categorization Schemes and Cycler/>

8.9 Introspective Classification Rules，Calibration，and Correlated Equilibrium

8.10 Sonsino’S Model of Pattern Recognition

8.11 Manipulating Learning Procedurer/>

Referencer/>

Index

朱·弗登伯格 (Drew Fudenberg)

世界著名的美国经济学大师，美国国家科学院和美国人文与科学院院士，麻省理工学院经济系教授，主要研究领域为博弈论和动态经济学。1978年获哈佛大学应用数学学士后进入麻省理工学院继续深造，于1981年获得经济学博士学位，曾经在加州大学伯克利分校（1981年-1987年）、麻省理工学院（1987年-1993年，2016年至今）、哈佛大学（1993年-2016年）任教。他和诺贝尔经济学奖得主让·梯若尔教授合著的《博弈论》是全球范围内流行广泛的博弈论教材，《博弈论》也已被引进在“世界博弈论经典”系列中一起出版。

戴维·K. 莱文 (David K. Levine)

欧盟大学研究院经济学系和罗伯特·舒曼高等研究中心的联合主席以及美国圣路易斯华盛顿大学经济系杰出荣休教授。1977年获得加州大学洛杉矶分校的数学学士学位和经济学硕士学位，后进入麻省理工学院继续深造，于1981年获得经济学博士学位，曾经在加州大学洛杉矶分校任教25年并担任经济系主任。

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书籍介绍

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。