Cold Play: Learning Across Bimatrix Games

Lensberg, Terje; Schenk-Hoppé, Klaus Reiner

doi:10.2139/ssrn.3432903

Cold Play: Learning Across Bimatrix Games

50 Pages Posted: 8 Aug 2019 Last revised: 14 Mar 2020

See all articles by Terje Lensberg

Terje Lensberg

Norwegian School of Economics (NHH) - Department of Finance

Klaus Reiner Schenk-Hoppé

University of Manchester - Department of Economics; Norwegian School of Economics (NHH) - Department of Finance

Date Written: March 10, 2020

Abstract

We study one-shot play in the set of all bimatrix games by a large population of agents. The agents never see the same game twice, but they can learn 'across games' by developing solution concepts that tell them how to play new games. Each agent's individual solution concept is represented by a computer program, and natural selection is applied to derive stochastically stable solution concepts. Our aim is to develop a theory predicting how experienced agents would play in one-shot games.

Keywords: One-shot games, solution concepts, genetic programming, evolutionary stability

JEL Classification: C63, C73, C90

Suggested Citation: Suggested Citation

Lensberg, Terje and Schenk-Hoppé, Klaus Reiner, Cold Play: Learning Across Bimatrix Games (March 10, 2020). Available at SSRN: https://ssrn.com/abstract=3432903 or http://dx.doi.org/10.2139/ssrn.3432903