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Rage Against the Machines: How Subjects Learn to Play Against Computers

Listed author(s):
  • Burkhard C. Schipper
  • Jorg Oechssler
  • Albert Kolb

    (Department of Economics, University of California Davis)

We use an experiment to explore how subjects learn to play against computers which are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was conducted, both, on the internet and in the usual laboratory setting. We find some systematic differences, which however can be traced to the different incentives structures rather than the experimental environment.

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File URL: http://wp.econ.ucdavis.edu/05-16.pdf
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Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 516.

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Length: 43
Date of creation: 12 Oct 2005
Handle: RePEc:cda:wpaper:05-16
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Web page: http://www.econ.ucdavis.edu
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  17. Schipper, Burkhard C, 2011. "Strategic control of myopic best reply in repeated games," MPRA Paper 30219, University Library of Munich, Germany.
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