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Learning about learning in games through experimental control of strategic interdependence
Jason Shachat , J. Todd Swarthout
#002031 20131014 (published) Views:5
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 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-Codes: C72, C92, C81
Keywords: Learning, Repeated games, Experiments, Simulation


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