Learning about learning in games through experimental control of strategic interdependence

Jason Shachat and J. Todd Swarthout

No 2013-10-14, Working Papers from Wang Yanan Institute for Studies in Economics (WISE), Xiamen University

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 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 algorithms' best response correspondences.

Keywords: Learning; Repeated games; Experiments; Simulation (search for similar items in EconPapers)
JEL-codes: C72 C81 C92 (search for similar items in EconPapers)
Date: 2013-10-14
New Economics Papers: this item is included in nep-cmp, nep-cse, nep-evo, nep-gth and nep-hpe
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Related works:
Journal Article: Learning about learning in games through experimental control of strategic interdependence (2012)
Working Paper: Learning about learning in games through experimental control of strategic interdependence (2011)
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: https://EconPapers.repec.org/RePEc:wyi:wpaper:002031

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