We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either Roth and Erev's reinforcement learning algorithm or Camerer and Ho's EWA algorithm. The human/algorithm interaction provides results that can't be obtained from the analysis of pure human interactions or model simulations. The learning algorithms are more sensitive than humans in calculating exploitable opponent play. Learning algorithms respond to these calculated opportunities systematically; however, the magnitude of these responses are too weak to improve the algorithm's payoffs. Human play against various decision maker types does not significantly vary. These results demonstrate that humans and currently proposed models of their behavior differ in that humans do not adjust payoff assessments by smooth transition functions and that when humans detect exploitable play they are more likely to choose the best response to this belief.
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Paper provided by EconWPA in its series Experimental with number
0310003.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Robert W. Rosenthal & Jason Shachat & Mark Walker, 2003.
"Hide and Seek in Arizona,"
Experimental
0312001, EconWPA.
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Peter Dürsch & Albert Kolb & Jörg Oechssler & Burkhard C. Schipper, 2005.
"Rage Against the Machines: How Subjects Learn to Play Against Computers,"
Discussion Papers
63, SFB/TR 15 Governance and the Efficiency of Economic Systems, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
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