Almost-Rational Learning of Nash Equilibrium without Absolute Continuity
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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- John Nachbar, 2010.
"Prediction, Optimization and Learning in Repeated Games,"
Levine's Working Paper Archive
576, David K. Levine.
- John H. Nachbar, 1997. "Prediction, Optimization, and Learning in Repeated Games," Econometrica, Econometric Society, vol. 65(2), pages 275-310, March.
- John H. Nachbar, 1995. "Prediction, Optimization, and Learning in Repeated Games," Game Theory and Information 9504001, EconWPA, revised 14 Feb 1996.
- 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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