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Learning to alternate

Arifovic, Jasmina and Ledyard, John O. (2018) Learning to alternate. Experimental Economics, 21 (3). pp. 692-721. ISSN 1386-4157. doi:10.1007/s10683-018-9568-1.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Arifovic, Jasmina0000-0002-7092-6541
Additional Information:© 2018 Economic Science Association. Received: 12 January 2016; Revised: 3 January 2018; Accepted: 19 March 2018; Published online: 12 April 2018. 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.
Group:Social Science Working Papers
Funding AgencyGrant Number
Gordon and Betty Moore Foundation1158
Subject Keywords:Battle of Sexes; Alternation; Learning
Other Numbering System:
Other Numbering System NameOther Numbering System ID
Social Science Working Paper1437
Issue or Number:3
Classification Code:JEL Classification: C72; C73; D83
Record Number:CaltechAUTHORS:20180718-090852064
Persistent URL:
Official Citation:Arifovic, J. & Ledyard, J. Exp Econ (2018) 21: 692.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87950
Deposited By: Tony Diaz
Deposited On:18 Jul 2018 18:12
Last Modified:16 Nov 2021 00:22

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