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Learning to alternate

Jasmina Arifovic () and John Ledyard ()
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Jasmina Arifovic: Simon Fraser University
John Ledyard: California Institute of Technology

Experimental Economics, 2018, vol. 21, issue 3, 692-721

Abstract: 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 Sexes; Alternation; Learning (search for similar items in EconPapers)
JEL-codes: C72 C73 D83 (search for similar items in EconPapers)
Date: 2018
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