Entropified Berk-Nash Equilibrium

22 Pages Posted: 30 Oct 2019 Last revised: 1 Nov 2019

See all articles by Filippo Massari

Filippo Massari

Bocconi University - Department of Decision Sciences

Jonathan Newton

Kyoto University - Institute of Economic Research

Date Written: October 31, 2019

Abstract

Esponda and Pouzo (2016) propose Berk-Nash equilibrium as a solution concept for games that are misspecified in that it is impossible for players to learn the true probability distribution over outcomes. In general, the beliefs that support Berk-Nash equilibrium are not stable: there may exist a profitable deviation to alternative beliefs in the player’s support that lead to higher payoffs. From an evolutionary perspective, this renders the beliefs that support Berk-Nash vulnerable to invasion. Drawing on the machine learning literature, we propose robust Berk-Nash equilibrium, which is immune to this critique.

Keywords: misspecified learning, evolutionary models, Berk-Nash Equilibrium

JEL Classification: D8, C7, C4

Suggested Citation

Massari, Filippo and Newton, Jonathan, Entropified Berk-Nash Equilibrium (October 31, 2019). Available at SSRN: https://ssrn.com/abstract=3473630

Filippo Massari (Contact Author)

Bocconi University - Department of Decision Sciences ( email )

Via Roentgen 1
Milan, 20136
Italy

Jonathan Newton

Kyoto University - Institute of Economic Research ( email )

Yoshida-Honmachi
Sakyo-ku
Kyoto 606-8501
JAPAN

Register to save articles to
your library

Register

Paper statistics

Downloads
8
Abstract Views
60
PlumX Metrics