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The Theory of Learning in Games
 
 
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The Theory of Learning in Games [Hardcover]

Drew Fudenberg (Author), David K. Levine (Author)
4.0 out of 5 stars  See all reviews (2 customer reviews)
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"This book collects the essential existing results in thefast-paced field of learning and evolutionary game theory togetherwith new work by two leaders in the field. It will be essential toanyone doing theoretical work on learning and games or usingevoultionary game theory in applied work." Larry Samuelson , Antoine Augustin Cournot Professor of Economics, University of Wisconsin

Book Description

In economics, most noncooperative game theory has focused on equilibrium in games, especially Nash equilibrium and its refinements. The traditional explanation for when and why equilibrium arises is that it results from analysis and introspection by the players in a situation where the rules of the game, the rationality of the players, and the players' payoff functions are all common knowledge. Both conceptually and empirically, this theory has many problems.In The Theory of Learning in Games Drew Fudenberg and David Levine develop an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time. The models they explore provide a foundation for equilibrium theory and suggest useful ways for economists to evaluate and modify traditional equilibrium concepts.

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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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4.0 out of 5 stars (2 customer reviews)
 
 
 
 
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4.0 out of 5 stars Learning Learning in Games, Jan 27 2003
By 
Joseph Clark (Brisbane, QLD. Australia) - See all my reviews
This review is from: The Theory of Learning in Games (Hardcover)
An excellent treatise on some important work in the theory of learning in games. Fudenberg and Levine provide a good coverage of standard myopic play dynamics with a special emphasis on ficticious play and replicator dynamics. I particularly liked the sections going through the Kandori, Mailath and Rob (1993) model as well as Young (1993) on the evolution of convention.

The treatments of dynamic systems analysis, elementary game theory, stochastic approximation theory, etc., are necessarily short. The appendices do not suffice for a reader without a reasonable background.

Nonetheless an essential read for anybody doing serious work in learning, or wanting to know what all the fuss is about.

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4.0 out of 5 stars Good book, July 26 2000
By 
Mihailo Despotovic (Silicon Valley, CA USA) - See all my reviews
(REAL NAME)   
This review is from: The Theory of Learning in Games (Hardcover)
During the work on my master thesis ("Learning in strategic games") i bought several books about the topic. This is the one of them. Chapters 1 and 2 (Introduction, Fictitious Play) are really good introduction into the subject. The following chapters evolve the theory further giving some good ideas for practical implementation (I was writing a C program which had to be able to play the game and to learn). I would recommend this book to anyone interested in relatively new field - Learning in games.
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Amazon.com: 3.0 out of 5 stars (3 customer reviews)

19 of 25 people found the following review helpful
4.0 out of 5 stars Good book, July 26 2000
By Mihailo Despotovic - Published on Amazon.com
This review is from: The Theory of Learning in Games (Hardcover)
During the work on my master thesis ("Learning in strategic games") i bought several books about the topic. This is the one of them. Chapters 1 and 2 (Introduction, Fictitious Play) are really good introduction into the subject. The following chapters evolve the theory further giving some good ideas for practical implementation (I was writing a C program which had to be able to play the game and to learn). I would recommend this book to anyone interested in relatively new field - Learning in games.

7 of 9 people found the following review helpful
4.0 out of 5 stars Learning Learning in Games, Jan 27 2003
By Joseph Clark - Published on Amazon.com
This review is from: The Theory of Learning in Games (Hardcover)
An excellent treatise on some important work in the theory of learning in games. Fudenberg and Levine provide a good coverage of standard myopic play dynamics with a special emphasis on ficticious play and replicator dynamics. I particularly liked the sections going through the Kandori, Mailath and Rob (1993) model as well as Young (1993) on the evolution of convention.

The treatments of dynamic systems analysis, elementary game theory, stochastic approximation theory, etc., are necessarily short. The appendices do not suffice for a reader without a reasonable background.

Nonetheless an essential read for anybody doing serious work in learning, or wanting to know what all the fuss is about.


7 of 18 people found the following review helpful
1.0 out of 5 stars This book is not about learning but the application of nonlinear dynamics, Sep 1 2005
By Zac - Published on Amazon.com
This review is from: The Theory of Learning in Games (Hardcover)
This book does not provide valuable information about learning systems. It demonstrates, that nonlinear dynamics can be used to describe a subclass of learning. I personally doubt, that this subclass is of great interest, because it neglects completely heuristic strategies in game playing. Besides this, nonlinear dynamics is only useful if the number of parameter of the system is small. I doubt, that these toy examples are sufficient to describe reality, e.g., economics.

Moreover, the organization of the book and the style it is written in, is in my view not favorable.

I guess, this book is for a very small readership that does not have to worry about the correspondence of a model with nature. But also from this perspective it can not be recommended, because it is not written well. Both thumbs down!
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