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

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Author Info

  • Peter Dürsch

    ()
    (University of Heidelberg, Department of Economics)

  • Albert Kolb

    (University of Bonn, Department of Economics)

  • Jörg Oechssler

    ()
    (University of Heidelberg, Department of Economics)

  • Burkhard C. Schipper

    ()
    (University of California, Department of Economics)

Abstract

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://www.uni-heidelberg.de/md/awi/forschung/dp423.pdf
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Bibliographic Info

Paper provided by University of Heidelberg, Department of Economics in its series Working Papers with number 0423.

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Length: 43 pages
Date of creation: Oct 2005
Date of revision: Oct 2005
Handle: RePEc:awi:wpaper:0423

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Related research

Keywords: learning; fictitious play; imitation; reinforcement; trial & error; strategic teaching; Cournot duopoly; experiments; internet.;

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References

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Citations

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Cited by:
  1. repec:fee:wpaper:1101 is not listed on IDEAS
  2. Burkhard C. Schipper, 2005. "Imitators and Optimizers in Cournot oligopoly," Working Papers 537, University of California, Davis, Department of Economics.
  3. Shachat, Jason & Swarthout, J. Todd, 2012. "Learning about learning in games through experimental control of strategic interdependence," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 383-402.
  4. Schipper, Burkhard C, 2011. "Strategic control of myopic best reply in repeated games," MPRA Paper 30219, University Library of Munich, Germany.
  5. Peter Duersch & Albert Kolb & Jörg Oechssler & Burkhard Schipper, 2010. "Rage against the machines: how subjects play against learning algorithms," Economic Theory, Springer, vol. 43(3), pages 407-430, June.
  6. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
  7. repec:hal:cesptp:hal-00607223 is not listed on IDEAS
  8. Bigoni, Maria, 2010. "What do you want to know? Information acquisition and learning in experimental Cournot games," Research in Economics, Elsevier, vol. 64(1), pages 1-17, March.
  9. repec:hal:cesptp:halshs-00145436 is not listed on IDEAS
  10. Terracol, Antoine & Vaksmann, Jonathan, 2009. "Dumbing down rational players: Learning and teaching in an experimental game," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 54-71, May.

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