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.
Drew Fudenberg is Professor of Economics at MIT.
David K. Levine is John H. Biggs Distinguished Professor of Economics at Washington University, St. Louis.
A masterly introduction to a wide range of recent models of learning and evolution in games. The models are very nicely explained, exemplified and related to each other.
Joergen W. Weibull, Economics Department, Stockholm School of Economics
Written by two prominent contributors to the exploding literature on evolution and learning in economics and game theory, this excellent book is exceptionally comprehensive. It will be useful for graduate students and advanced researchers alike.
Eddie Dekel, Northwestern University
This book collects the essential existing results in the fast-paced field of learning and evolutionary game theory together with new work by two leaders in the field. It will be essential to anyone doing theoretical work on learning and games or using evoultionary game theory in applied work.
Larry Samuelson, Antoine Augustin Cournot Professor of Economics, University of Wisconsin