Learning about learning in games through experimental control of strategic interdependence
Jason Shachat
Wang Yanan Institute for Studies in Economics (WISE), and the MOE Key Laboratory in Econometerics, Xiamen University, China
J. Todd Swarthout
Department of Economics, Georgia State University, Atlanta, USA
6/27/2011 3:27:53 PM
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 oppor-tunities 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.
Learning, Repeated games, Experiments, Simulation