Learning and equilibrium in misspecified models

18 Pages Posted: 30 Oct 2019 Last revised: 30 Jun 2020

See all articles by Filippo Massari

Filippo Massari

Bocconi University - Department of Decision Sciences; University of East Anglia

Jonathan Newton

Kyoto University - Institute of Economic Research

Date Written: June 30, 2020

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.

Keywords: misspecified learning, evolutionary models

JEL Classification: D8, C7, C4

Suggested Citation

Massari, Filippo and Newton, Jonathan, Learning and equilibrium in misspecified models (June 30, 2020). Available at SSRN: https://ssrn.com/abstract=3473630 or http://dx.doi.org/10.2139/ssrn.3473630

Filippo Massari (Contact Author)

Bocconi University - Department of Decision Sciences ( email )

Via Roentgen 1
Milan, 20136
Italy

University of East Anglia ( email )

Norwich Research Park
Norwich, Norfolk NR4 7TJ
United Kingdom

Jonathan Newton

Kyoto University - Institute of Economic Research ( email )

Yoshida-Honmachi
Sakyo-ku
Kyoto 606-8501
JAPAN

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