Learning and equilibrium in misspecified models

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

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Abstract

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
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Behavioural Economics Faculty of Social Sciences > Research Groups > Economic Theory
Related URLs:	https://papers.ssrn.com/sol3/papers.cfm?...
Depositing User:	LivePure Connector
Date Deposited:	23 Jul 2020 23:49
Last Modified:	21 Mar 2023 14:32
URI:	https://ueaeprints.uea.ac.uk/id/eprint/76263
DOI:

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