Publication details for Prof Jason ShachatShachat, J. & Swarthout, J.T. (2012). Learning about learning in games through experimental control of strategic interdependence. Journal of Economic Dynamics and Control 36(3): 383-402.
- Publication type: Journal Article
- ISSN/ISBN: 0165-1889
- DOI: 10.1016/j.jedc.2011.09.007
- Further publication details on publisher web site
Author(s) from Durham
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.