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Prediction, Optimization, and Learning in Repeated Games

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  • John H. Nachbar

Abstract

This 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.

Suggested Citation

  • John H. Nachbar, 1997. "Prediction, Optimization, and Learning in Repeated Games," Econometrica, Econometric Society, vol. 65(2), pages 275-310, March.
    • Handle: RePEc:ecm:emetrp:v:65:y:1997:i:2:p:275-310
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    References listed on IDEAS

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    1. Kalai, Ehud & Stanford, William, 1988. "Finite Rationality and Interpersonal Complexity in Repeated Games," Econometrica, Econometric Society, vol. 56(2), pages 397-410, March.
    2. Binmore, Ken, 1987. "Modeling Rational Players: Part I," Economics and Philosophy, Cambridge University Press, vol. 3(2), pages 179-214, October.
    3. Lawrence Blume & David Easley, 1993. "Rational Expectations and Rational Learning," Game Theory and Information 9307003, University Library of Munich, Germany.
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    More about this item

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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