Learning About Learning in Games Through Experimental Control of Strategic Interdependence
45 Pages Posted: 3 Sep 2008
Date Written: August 2008
We report experiments in which humans repeatedly play one of two games against a computer program that follows either a reinforcement learning or an Experience Weighted Attraction algorithm. Our experiments show 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 algorithm's best response correspondence. These properties are revealed only by our human versus computer experiments, and not by our standard human versus human experiments, nor our model simulations.
Keywords: learning, repeated games, experiments, simulation
JEL Classification: C72, C92, C81
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Cultural Differences in Ultimatum Game Experiments: Evidence from a Meta-Analysis
By Hessel Oosterbeek, Randolph Sloof, ...
Cooperation and Noise in Public Goods Experiments: Applying the Contribution Function Approach
How Universal is Behavior? A Four Country Comparison of Spite, Cooperation and Errors in Voluntary Contribution Mechanisms
By Jordi Brandts, Tatsuyoshi Saijo, ...
By Bertrand Munier, Reinhard Selten, ...
The Value of Non-Binding Announcements in Public Goods Experiments: Some Theory and Experimental Evidence
By Michael Berlemann, Marcus Dittrich, ...
Decision-Making: A Neuroeconomic Perspective
Auctions and Fair Division Games - a Cross-Country Bidding Experiment