Learning and equilibrium in misspecified models

Massari, Filippo and Newton, Jonathan (2020) Learning and equilibrium in misspecified models.

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We consider learning in games that are misspecified in that players are unable to learn the true probability distribution over outcomes. Under misspecification, Bayes rule might not converge to the model that leads to actions with the highest objective payoff among the models subjectively admitted by the player. From an evolutionary perspective, this renders a population of Bayesians vulnerable to invasion. Drawing on the machine learning literature, we show that learning rules that outperform Bayes’ rule suggest a new solution concept for misspecified games: misspecified Nash equilibrium.

Item Type: Article
Faculty \ School: Faculty of Social Sciences > School of Economics
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Depositing User: LivePure Connector
Date Deposited: 23 Jul 2020 23:49
Last Modified: 28 Feb 2021 00:35
URI: https://ueaeprints.uea.ac.uk/id/eprint/76263

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