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

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Author Info

  • Peter Duersch

    (Department of Economics, University of Heidelberg)

  • Albert Kolb

    (Department of Economics, University of Bonn)

  • Joerg Oechssler

    (Department of Economics, University of Heidelberg)

  • Burkhard Schipper

    (Department of Economics, University of California)

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://128.118.178.162/eps/game/papers/0510/0510012.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Game Theory and Information with number 0510012.

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Length: 43 pages
Date of creation: 25 Oct 2005
Date of revision:
Handle: RePEc:wpa:wuwpga:0510012

Note: Type of Document - pdf; pages: 43
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Web page: http://128.118.178.162

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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  1. Burkhard Schipper, 2002. "Imitators and Optimizers in Cournot Oligopoly," Bonn Econ Discussion Papers bgse29_2002, University of Bonn, Germany.
  2. Ellison, Glenn, 1997. "Learning from Personal Experience: One Rational Guy and the Justification of Myopia," Games and Economic Behavior, Elsevier, vol. 19(2), pages 180-210, May.
  3. Steffen Huck & Hans-Theo Normann & Joerg Oechssler, 1997. "Learning in Cournot Oligopoly - An Experiment," Game Theory and Information 9707009, EconWPA, revised 22 Jul 1997.
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  7. Jose Apesteguia & Steffen Huck & Jörg Oechssler, 2003. "Imitation - Theory and Experimental Evidence," Bonn Econ Discussion Papers bgse20_2003, University of Bonn, Germany, revised Aug 2004.
  8. repec:fee:wpaper:1103 is not listed on IDEAS
  9. Mathias Drehmann & J�rg Oechssler & Andreas Roider, 2005. "Herding and Contrarian Behavior in Financial Markets: An Internet Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1403-1426, December.
  10. Rajiv Sarin & Farshid Vahid, 2004. "Strategy Similarity and Coordination," Economic Journal, Royal Economic Society, vol. 114(497), pages 506-527, 07.
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  12. Offerman, T.J.S. & Potters, J.J.M. & Sonnemans, J., 2002. "Imitation and belief learning in an oligopoly experiment," Open Access publications from Tilburg University urn:nbn:nl:ui:12-91663, Tilburg University.
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  20. Burkhard Schipper, 2011. "Strategic Control of Myopic Best Reply in Repeated Games," Working Papers 115, University of California, Davis, Department of Economics.
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  24. Kirchkamp, Oliver & Nagel, Rosemarie, 2005. "Learning and cooperation in network experiments," Sonderforschungsbereich 504 Publications 05-27, Sonderforschungsbereich 504, Universität Mannheim & Sonderforschungsbereich 504, University of Mannheim.
  25. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
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Citations

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Cited by:
  1. Antoine Terracol & Jonathan Vaksmann, 2007. "Dumbing down rational players : learning and teaching in an experimental game," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00145436, HAL.
  2. Burkhard C. Schipper, 2005. "Imitators and Optimizers in Cournot oligopoly," Working Papers 537, University of California, Davis, Department of Economics.
  3. 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.
  4. 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.
  5. repec:fee:wpaper:1103 is not listed on IDEAS
  6. repec:fee:wpaper:1101 is not listed on IDEAS
  7. Jason Shachat & J. Todd Swarthout, 2003. "Learning about Learning in Games through Experimental Control of Strategic Interdependence," Experimental 0310003, EconWPA.
  8. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
  9. 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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