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

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  • Peter Dürsch
  • Albert Kolb
  • Jörg Oechssler
  • Burkhard C. Schipper

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.wiwi.uni-bonn.de/bgsepapers/bonedp/bgse31_2005.pdf
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Bibliographic Info

Paper provided by University of Bonn, Germany in its series Bonn Econ Discussion Papers with number bgse31_2005.

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Length: 45
Date of creation: Oct 2005
Date of revision:
Handle: RePEc:bon:bonedp:bgse31_2005

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Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
Fax: +49 228 73 6884
Web page: http://www.bgse.uni-bonn.de

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:1103 is not listed on IDEAS
  2. Jason Shachat & J. Todd Swarthout, 2002. "Learning about Learning in Games through Experimental Control of Strategic Interdependence," Experimental Economics Center Working Paper Series 2006-17, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University, revised Aug 2008.
  3. Burkhard Schipper, 2002. "Imitators and Optimizers in Cournot Oligopoly," Bonn Econ Discussion Papers bgse29_2002, University of Bonn, Germany.
  4. repec:fee:wpaper:1101 is not listed on IDEAS
  5. 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.
  6. Schipper, Burkhard C, 2011. "Strategic control of myopic best reply in repeated games," MPRA Paper 30219, University Library of Munich, Germany.
  7. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
  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. 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.

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