Jason Shachata, J. Todd Swarthoutba
Wang Yanan Institute for Studies in Economics (WISE), and the MOE Key Laboratory in Econometerics, Xiamen University, China
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 payo -increasing oppor-
tunities systematically; however, the responses are too weak to improve the algorithms' payo s.
Human play against various decision maker types doesn't vary signi cantly. 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.
Keywords: Learning, Repeated games, Experiments, Simulation
JEL: C72, C92, C81
