Learning about learning in games through experimental control of strategic interdependence

Jason Shachat () and J. Todd Swarthout

Journal of Economic Dynamics and Control, 2012, vol. 36, issue 3, pages 383-402

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.

Keywords: Learning; Repeated games; Experiments; Simulation (search for similar items in EconPapers)
JEL-codes: C72 C92 C81 (search for similar items in EconPapers)
Date: 2012
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Related works:
Working Paper: Learning about Learning in Games through Experimental Control of Strategic Interdependence (2008)
Working Paper: Learning about Learning in Games through Experimental Control of Strategic Interdependence (2003)
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Persistent link: http://EconPapers.repec.org/RePEc:eee:dyncon:v:36:y:2012:i:3:p:383-402

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