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Learning to alternate

Published online by Cambridge University Press:  14 March 2025

Jasmina Arifovic  and
John Ledyard
Jasmina Arifovic*
Affiliation:
Simon Fraser University, Burnaby, Canada
John Ledyard*
Affiliation:
California Institute of Technology, Pasadena, USA
*
arifovic@sfu.ca
jledyard@caltech.edu
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Abstract

The Individual Evolutionary Learning (IEL) model explains human subjects’ behavior in a wide range of repeated games which have unique Nash equilibria. Using a variation of ‘better response’ strategies, IEL agents quickly learn to play Nash equilibrium strategies and their dynamic behavior is like that of humans subjects. In this paper we study whether IEL can also explain behavior in games with gains from coordination. We focus on the simplest such game: the 2 person repeated Battle of Sexes game. In laboratory experiments, two patterns of behavior often emerge: players either converge rapidly to one of the stage game Nash equilibria and stay there or learn to coordinate their actions and alternate between the two Nash equilibria every other round. We show that IEL explains this behavior if the human subjects are truly in the dark and do not know or believe they know their opponent’s payoffs. To explain the behavior when agents are not in the dark, we need to modify the basic IEL model and allow some agents to begin with a good idea about how to play. We show that if the proportion of inspired agents with good ideas is chosen judiciously, the behavior of IEL agents looks remarkably similar to that of human subjects in laboratory experiments.

Keywords

Battle of SexesAlternationLearning

JEL classification

C73: Stochastic and Dynamic Games • Evolutionary Games • Repeated Games D83: Search • Learning • Information and Knowledge • Communication • Belief • Unawareness C72: Noncooperative Games
Type
Original Paper
Information
Experimental Economics , Volume 21 , Issue 3: Special Issue in Honor of John Van Huyck , September 2018 , pp. 692 - 721
Copyright
Copyright © 2018 Economic Science Association

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10683-018-9568-1) contains supplementary material, which is available to authorized users.

We thank Sarah Deretic, Kevin James, Brian Merlob and Heng Sok for their excellent research assistance. We would also like to thank John Duffy, Tim Cason, Julian Romero, participants at the Workshop in Memory of John van Huyck, Southern Methodist University, 2015, participants at the Southern Economic Association Meetings, New Orleans, 2015, as well as two referees and an editor. Jasmina Arifovic gratefully acknowledges financial support from CIGI-INET Grant #5553. John Ledyard thanks the Moore Foundation whose grant to Caltech for Experimentation with Large, Diverse and Interconnected Socio-Economic Systems, Award #1158, supported the experimental work.

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