Learning about learning in games through experimental control of strategic interdependence

Jason Shachat and J. Todd Swarthout

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.

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C72, C92, C81

Learning, Repeated games, Experiments, Simulation

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Full Text (2011)