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Hidden Experts in the Crowd: Using Meta-Predictions to Leverage Expertise in Single-Question Prediction Problems

Author

Listed:
  • Tom Wilkening

    (Department of Economics, The University of Melbourne, Parkville, Victoria 3010, Australia)

  • Marcellin Martinie

    (Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia)

  • Piers D. L. Howe

    (Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia)

Abstract

Modern forecasting algorithms use the wisdom of crowds to produce forecasts better than those of the best identifiable expert. However, these algorithms may be inaccurate when crowds are systematically biased or when expertise varies substantially across forecasters. Recent work has shown that meta-predictions—a forecast of the average forecasts of others—can be used to correct for biases even when no external information, such as forecasters’ past performance, is available. We explore whether meta-predictions can also be used to improve forecasts by identifying and leveraging the expertise of forecasters. We develop a confidence-based version of the Surprisingly Popular algorithm proposed by Prelec, Seung, and McCoy. As with the original algorithm, our new algorithm is robust to bias. However, unlike the original algorithm, our version is predicted to always weight forecasters with more informative private signals more than forecasters with less informative ones. In a series of experiments, we find that the modified algorithm does a better job in weighting informed forecasters than the original algorithm and show that individuals who are correct more often on similar decision problems contribute more to the final decision than other forecasters. Empirically, the modified algorithm outperforms the original algorithm for a set of 500 decision problems.

Suggested Citation

  • Tom Wilkening & Marcellin Martinie & Piers D. L. Howe, 2022. "Hidden Experts in the Crowd: Using Meta-Predictions to Leverage Expertise in Single-Question Prediction Problems," Management Science, INFORMS, vol. 68(1), pages 487-508, January.
  • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:1:p:487-508
    DOI: 10.1287/mnsc.2020.3919
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    References listed on IDEAS

    as
    1. David V. Budescu & Eva Chen, 2015. "Identifying Expertise to Extract the Wisdom of Crowds," Management Science, INFORMS, vol. 61(2), pages 267-280, February.
    2. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    3. Johannes Müller-Trede & Shoham Choshen-Hillel & Meir Barneron & Ilan Yaniv, 2017. "The Wisdom of Crowds in Matters of Taste," Discussion Paper Series dp709, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    4. Ville A. Satopää & Robin Pemantle & Lyle H. Ungar, 2016. "Modeling Probability Forecasts via Information Diversity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1623-1633, October.
    5. Dražen Prelec & H. Sebastian Seung & John McCoy, 2017. "A solution to the single-question crowd wisdom problem," Nature, Nature, vol. 541(7638), pages 532-535, January.
    6. 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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