Learning About Learning in Games Through Experimental Control of Strategic Interdependence

45 Pages Posted: 3 Sep 2008

See all articles by Jason M. Shachat

Jason M. Shachat

National University of Singapore (NUS) - Department of Economics

J. Todd Swarthout

Georgia State University - Andrew Young School of Policy Studies

Date Written: August 2008

Abstract

We report experiments in which humans repeatedly play one of two games against a computer program that follows either a reinforcement learning or an Experience Weighted Attraction algorithm. Our experiments show these learning algorithms detect exploitable opportunities more sensitively than humans. Also, learning algorithms respond to detected payoff-increasing opportunities systematically; however, the responses are too weak to improve the algorithms' payoffs. Human play against various decision maker types doesn't vary significantly. These factors lead to a strong linear relationship between the humans' and algorithms' action choice proportions that is suggestive of the algorithm's best response correspondence. These properties are revealed only by our human versus computer experiments, and not by our standard human versus human experiments, nor our model simulations.

Keywords: learning, repeated games, experiments, simulation

JEL Classification: C72, C92, C81

Suggested Citation

Shachat, Jason and Swarthout, J. Todd, Learning About Learning in Games Through Experimental Control of Strategic Interdependence (August 2008). Andrew Young School of Policy Studies Research Paper Series No. 08-28, Available at SSRN: https://ssrn.com/abstract=1260867 or http://dx.doi.org/10.2139/ssrn.1260867

Jason Shachat

National University of Singapore (NUS) - Department of Economics ( email )

1 Arts Link, AS2 #06-02
Singapore 117570, Singapore 119077
Singapore

J. Todd Swarthout (Contact Author)

Georgia State University - Andrew Young School of Policy Studies ( email )

P.O. Box 3992
Atlanta, GA 30302-3992
United States

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