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

  • 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)

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://econwpa.repec.org/eps/game/papers/0510/0510012.pdf
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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
Contact details of provider: Web page: http://econwpa.repec.org

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