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Research

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Publication details for Prof Jason Shachat

Shachat, J. & Swarthout, J.T. (2012). Learning about learning in games through experimental control of strategic interdependence. Journal of Economic Dynamics and Control 36(3): 383-402.

Author(s) from Durham

Abstract

We report results from an experiment in which humans repeatedly play one of two games against a computer program that follows either a reinforcement or an experience weighted attraction learning algorithm. Our experiment shows 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 does not vary significantly. These factors lead to a strong linear relationship between the humans' and algorithms' action choice proportions that is suggestive of the algorithms' best response correspondences.