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Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Paul J. Healy & Sera Linardi & J. Richard Lowery & John O. Ledyard, 2010. "Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders," Management Science, INFORMS, vol. 56(11), pages 1977-1996, November.
  • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:11:p:1977-1996
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    File URL: http://dx.doi.org/10.1287/mnsc.1100.1226
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Florian Teschner & David Rothschild & Henner Gimpel, 2017. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 26(5), pages 953-971, September.
    2. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    3. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
    4. Abraham Othman & Tuomas Sandholm, 2013. "The Gates Hillman prediction market," Review of Economic Design, Springer;Society for Economic Design, vol. 17(2), pages 95-128, June.
    5. Halim, Edward & Riyanto, Yohanes Eko & Roy, Nilanjan, 2017. "Costly Information Acquisition, Social Networks and Asset Prices: Experimental Evidence," MPRA Paper 80658, University Library of Munich, Germany.
    6. John J. Nay & Martin Van der Linden & Jonathan M. Gilligan, 2016. "Betting and Belief: Prediction Markets and Attribution of Climate Change," Papers 1603.08961, arXiv.org, revised Jul 2016.
    7. Lian Jian & Rahul Sami, 2012. "Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution," Management Science, INFORMS, vol. 58(1), pages 123-140, January.
    8. Yusufcan Masatlioglu & Sarah Taylor & Neslihan Uler, 2012. "Behavioral mechanism design: evidence from the modified first-price auctions," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 159-173, September.
    9. Linardi, Sera, 2017. "Accounting for noise in the microfoundations of information aggregation," Games and Economic Behavior, Elsevier, vol. 101(C), pages 334-353.
    10. Peeters R.J.A.P. & Wolk K.L., 2014. "Eliciting and aggregating individual expectations: An experimental study," Research Memorandum 029, Maastricht University, Graduate School of Business and Economics (GSBE).
    11. repec:spr:grdene:v:27:y:2018:i:2:d:10.1007_s10726-018-9565-y is not listed on IDEAS

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