Prediction, Optimization, and Learning in Repeated Games
AbstractThis paper shows that, in many infinitely repeated games, if players optimize with respect to beliefs that satisfy a diversity condition termed neutrality, then each player will choose a strategy that his opponent was certain would not be played. This is an obstacle to formulation of a learning theory in which Nash equilibrium behavior is a necessary long-run consequence of optimization by cautious players.
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Bibliographic InfoArticle provided by Econometric Society in its journal Econometrica.
Volume (Year): 65 (1997)
Issue (Month): 2 (March)
Other versions of this item:
- John H. Nachbar, 1995. "Prediction, Optimization, and Learning in Repeated Games," Game Theory and Information 9504001, EconWPA, revised 14 Feb 1996.
- John Nachbar, 2010. "Prediction, Optimization and Learning in Repeated Games," Levine's Working Paper Archive 576, David K. Levine.
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Lawrence Blume & David Easley, 1993. "Rational Expectations and Rational Learning," Game Theory and Information 9307003, EconWPA.
- Binmore, Ken, 1987. "Modeling Rational Players: Part I," Economics and Philosophy, Cambridge University Press, vol. 3(02), pages 179-214, October.
- Ehud Kalai & William Stanford, 1986.
"Finite Rationality and Interpersonal Complexity in Repeated Games,"
679, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Kalai, Ehud & Stanford, William, 1988. "Finite Rationality and Interpersonal Complexity in Repeated Games," Econometrica, Econometric Society, vol. 56(2), pages 397-410, March.
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