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A Pari-Mutuel-Like Mechanism for Information Aggregation: A Field Test inside Intel

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  • Benjamin J. Gillen
  • Charles R. Plott
  • Matthew Shum

Abstract

A new information aggregation mechanism (IAM), developed via laboratory experimental methods, is implemented inside Intel Corporation in a long-running field test. The IAM, incorporating features of pari-mutuel betting, is uniquely designed to collect and quantize as probability distributions dispersed, subjectively held information. IAM participants’ incentives support timely information revelation and the emergence of consensus beliefs over future outcomes. Empirical tests demonstrate the robustness of experimental results and the IAM’s practical usefulness in addressing real-world problems. The IAM’s predictive distributions forecasting sales are very accurate, especially for short horizons and direct sales channels, often proving more accurate than Intel’s internal forecast.

Suggested Citation

  • Benjamin J. Gillen & Charles R. Plott & Matthew Shum, 2017. "A Pari-Mutuel-Like Mechanism for Information Aggregation: A Field Test inside Intel," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1075-1099.
    • Handle: RePEc:ucp:jpolec:doi:10.1086/692714
    DOI: 10.1086/692714
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    Citations

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    Cited by:

    1. Brice Corgnet & Cary Deck & Mark Desantis & Kyle Hampton & Erik O Kimbrough, 2019. "Reconsidering Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Working Papers halshs-02146611, HAL.
    2. Brice Corgnet & Cary Deck & Mark DeSantis & Kyle Hampton & Erik O. Kimbrough, 2023. "When Do Security Markets Aggregate Dispersed Information?," Management Science, INFORMS, vol. 69(6), pages 3697-3729, June.
    3. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2022. "Manipulation and (Mis)trust in Prediction Markets," Management Science, INFORMS, vol. 68(9), pages 6716-6732, September.
    4. Corgnet, Brice & Deck, Cary & DeSantis, Mark & Porter, David, 2018. "Information (non)aggregation in markets with costly signal acquisition," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 286-320.
    5. Ahrash Dianat & Christoph Siemroth, 2021. "Improving decisions with market information: an experiment on corporate prediction markets," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 143-176, March.
    6. Boris Maciejovsky & David V. Budescu, 2020. "Too Much Trust in Group Decisions: Uncovering Hidden Profiles by Groups and Markets," Organization Science, INFORMS, vol. 31(6), pages 1497-1514, November.
    7. Christopher P. Chambers & Federico Echenique & Alan D. Miller, 2023. "Decreasing Impatience," American Economic Journal: Microeconomics, American Economic Association, vol. 15(3), pages 527-551, August.
    8. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    9. Ronald Peeters & Leonard Wolk, 2019. "Elicitation of expectations using Colonel Blotto," Experimental Economics, Springer;Economic Science Association, vol. 22(1), pages 268-288, March.
    10. David Court & Benjamin Gillen & Jordi McKenzie & Charles R. Plott, 2018. "Two information aggregation mechanisms for predicting the opening weekend box office revenues of films: Boxoffice Prophecy and Guess of Guesses," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(1), pages 25-54, January.

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