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Learning about learning in games through experimental control of strategic interdependence

id: 2031 Date: 20131014 status: published Times:
AuthorJason Shachat , J. Todd Swarthout
ContentWe 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 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 algorithms' best response correspondences.
JEL-CodesC72, C92, C81
KeywordsLearning, Repeated games, Experiments, Simulation
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