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Almost-rational learning of Nash equilibrium without absolute continuity

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.
Publication status:
Published

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Publisher:
University of Oxford
Series:
Department of Economics Discussion Paper Series
Publication date:
2012-04-01
Paper number:
602
Keywords:
Pubs id:
1143829
Local pid:
pubs:1143829
Deposit date:
2020-12-15

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