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The Promise of Prediction Contests

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  • Phillip E. Pfeifer
  • Yael Grushka-Cockayne
  • Kenneth C. Lichtendahl

Abstract

This article examines the prediction contest as a vehicle for aggregating the opinions of a crowd of experts. After proposing a general definition distinguishing prediction contests from other mechanisms for harnessing the wisdom of crowds, we focus on point-forecasting contests-contests in which forecasters submit point forecasts with a prize going to the entry closest to the quantity of interest. We first illustrate the incentive for forecasters to submit reports that exaggerate in the direction of their private information. Whereas this exaggeration raises a forecaster's mean squared error, it increases his or her chances of winning the contest. And in contrast to conventional wisdom, this nontruthful reporting usually improves the accuracy of the resulting crowd forecast. The source of this improvement is that exaggeration shifts weight away from public information (information known to all forecasters) and by so doing helps alleviate public knowledge bias. In the context of a simple theoretical model of overlapping information and forecaster behaviors, we present closed-form expressions for the mean squared error of the crowd forecasts which will help identify the situations in which point forecasting contests will be most useful.

Suggested Citation

  • Phillip E. Pfeifer & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "The Promise of Prediction Contests," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 264-270, November.
  • Handle: RePEc:taf:amstat:v:68:y:2014:i:4:p:264-270
    DOI: 10.1080/00031305.2014.937545
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    References listed on IDEAS

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    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    2. Kenneth C. Lichtendahl, Jr. & Robert L. Winkler, 2007. "Probability Elicitation, Scoring Rules, and Competition Among Forecasters," Management Science, INFORMS, vol. 53(11), pages 1745-1755, November.
    3. Bryan Clair & David Letscher, 2007. "Optimal Strategies for Sports Betting Pools," Operations Research, INFORMS, vol. 55(6), pages 1163-1177, December.
    4. Oliver Kim & Steve C. Lim & Kenneth W. Shaw, 2001. "The Inefficiency of the Mean Analyst Forecast as a Summary Forecast of Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 39(2), pages 329-335, September.
    5. Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2004. "Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms," Management Science, INFORMS, vol. 50(7), pages 983-994, July.
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    Citations

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    Cited by:

    1. Sepideh Bazazi & Jorina von Zimmermann & Bahador Bahrami & Daniel Richardson, 2019. "Self-serving incentives impair collective decisions by increasing conformity," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-12, November.
    2. Ronald Peeters & Fan Rao & Leonard Wolk, 2022. "Small group forecasting using proportional-prize contests," Theory and Decision, Springer, vol. 92(2), pages 293-317, March.
    3. Brown, Alasdair & Reade, J. James, 2019. "The wisdom of amateur crowds: Evidence from an online community of sports tipsters," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1073-1081.
    4. Yanwei Jia & Jussi Keppo & Ville Satopää, 2023. "Herding in Probabilistic Forecasts," Management Science, INFORMS, vol. 69(5), pages 2713-2732, May.
    5. Phillip E. Pfeifer, 2016. "The promise of pick-the-winners contests for producing crowd probability forecasts," Theory and Decision, Springer, vol. 81(2), pages 255-278, August.
    6. Cem Peker, 2023. "Extracting the collective wisdom in probabilistic judgments," Theory and Decision, Springer, vol. 94(3), pages 467-501, April.
    7. Keppo, Jussi & Satopää, Ville A., 2024. "Bayesian herd detection for dynamic data," International Journal of Forecasting, Elsevier, vol. 40(1), pages 285-301.

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