The possibility of Bayesian learning in repeated games

Thomas Norman

Games and Economic Behavior, 2022, vol. 136, issue C, 142-152

Abstract: In infinitely repeated games, Nachbar (1997, 2005) has shown that Bayesian learning of a restricted strategy set is inconsistent; the beliefs required to learn any element of such a set will lead best responses to lie outside of it in most games. But I establish here that Nash convergence of Bayesian learning requires only that optimal play (rather than any possible play) is learnable, and an appropriately modified notion of learnability is consistent in many of the games to which Nachbar's result applies. This means that rational learning of equilibrium is possible in an important class including coordination games, which I illustrate with two examples of positive learning results.

Keywords: Repeated games; Nash equilibrium; Bayesian learning; Rational learning; Consistency (search for similar items in EconPapers)
JEL-codes: C72 C73 D83 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:136:y:2022:i:c:p:142-152

DOI: 10.1016/j.geb.2022.09.002

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