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Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

  • Paul J. Healy

    ()

    (Department of Economics, The Ohio State University, Columbus, Ohio 43210)

  • Sera Linardi

    ()

    (Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125)

  • J. Richard Lowery

    ()

    (Finance Department, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712)

  • John O. Ledyard

    ()

    (Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125)

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings.

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File URL: http://dx.doi.org/10.1287/mnsc.1100.1226
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Article provided by INFORMS in its journal Management Science.

Volume (Year): 56 (2010)
Issue (Month): 11 (November)
Pages: 1977-1996

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Handle: RePEc:inm:ormnsc:v:56:y:2010:i:11:p:1977-1996
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