Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5098848 | Journal of Economic Dynamics and Control | 2012 | 20 Pages |
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 payoff-increasing opportunities systematically; however, the responses are too weak to improve the algorithms' payoffs. Human play against various decision maker types does not 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.
Related Topics
Physical Sciences and Engineering
Mathematics
Control and Optimization
Authors
Jason Shachat, J. Todd Swarthout,