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Bayesian Learning, Smooth Approximate Optimal Behavior, and Convergence to ε‐Nash Equilibrium

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  • Yuichi Noguchi

Abstract

In this paper, I construct players' prior beliefs and show that these prior beliefs lead the players to learn to play an approximate Nash equilibrium uniformly in any infinitely repeated slightly perturbed game with discounting and perfect monitoring. That is, given any ε > 0, there exists a (single) profile of players' prior beliefs that leads play to almost surely converge to an ε‐Nash equilibrium uniformly for any (finite normal form) stage game with slight payoff perturbation and any discount factor less than 1.

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  • Yuichi Noguchi, 2015. "Bayesian Learning, Smooth Approximate Optimal Behavior, and Convergence to ε‐Nash Equilibrium," Econometrica, Econometric Society, vol. 83, pages 353-373, January.
    • Handle: RePEc:wly:emetrp:v:83:y:2015:i::p:353-373
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    Cited by:

    1. Norman, Thomas W.L., 2022. "The possibility of Bayesian learning in repeated games," Games and Economic Behavior, Elsevier, vol. 136(C), pages 142-152.
    2. Jindani, Sam, 2022. "Learning efficient equilibria in repeated games," Journal of Economic Theory, Elsevier, vol. 205(C).

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