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Almost-Rational Learning of Nash Equilibrium without Absolute Continuity

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  • Thomas Norman

Abstract

If players learn to play an infinitely repeated game using Bayesian learning, it is known that their strategies eventually approximate Nash equilibria of the repeated game under an absolute-continuity assumption on their prior beliefs.� We suppose here that Bayesian learners do not start with such a "grain of truth", but with arbitrarily low probability they revise beliefs that are performing badly.� We show that this process converges in probability to a Nash equilibrium of the repeated game.

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File URL: http://www.economics.ox.ac.uk/materials/papers/5769/paper602.pdf
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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 602.

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Date of creation: 01 Apr 2012
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Handle: RePEc:oxf:wpaper:602

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Keywords: Repeated games; Nash equilibrium; Rational learning; Bayesian learning; Absolute continuity;

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  1. John H. Nachbar, 1997. "Prediction, Optimization, and Learning in Repeated Games," Econometrica, Econometric Society, vol. 65(2), pages 275-310, March.
  2. Ehud Lehrer & Sylvain Sorin, 1998. "-Consistent equilibrium in repeated games," International Journal of Game Theory, Springer, vol. 27(2), pages 231-244.
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