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Aggregation Mechanisms for Crowd Predictions

Author

Listed:
  • Stefan Palan

    (Department of Banking and Finance, University of Graz
    Department of Banking and Finance, University of Innsbruck)

  • Jürgen Huber

    (Department of Banking and Finance, University of Innsbruck)

  • Larissa Senninger

    (Department of Banking and Finance, University of Innsbruck)

Abstract

When the information of many individuals is pooled, the resulting aggregate often is a good predictor of unknown quantities or facts ("wisdom of crowds"). This aggregate predictor frequently outperforms the forecasts of experts or even the best individual forecast included in the aggregation process. However, an appropriate aggregation mechanism is considered crucial to reaping the benefits of a "wise crowd". Of the many possible ways to aggregate individual forecasts, we compare (uncensored and censored) mean and median, continuous double auction market prices and sealed bid-offer call market prices in a controlled experiment. We use an asymmetric information structure where subjects know different subsets of the total information needed to exactly calculate the asset value to be estimated. We find that prices from continuous double auction markets clearly outperform all alternative approaches for aggregating dispersed information and that information is only useful to the best-informed subjects.

Suggested Citation

  • Stefan Palan & Jürgen Huber & Larissa Senninger, 2019. "Aggregation Mechanisms for Crowd Predictions," Working Paper Series, Social and Economic Sciences 2019-01, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
  • Handle: RePEc:grz:wpsses:2019-01
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    Cited by:

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    2. 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.
    3. Brünner, Tobias & Levinsky, Rene, 2020. "Price discovery and gains from trade in asset markets with insider trading," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224618, Verein für Socialpolitik / German Economic Association.
    4. Marco Mantovani & Antonio Filippin, 2024. "When do prediction markets return average beliefs? Experimental evidence," Working Papers 532, University of Milano-Bicocca, Department of Economics.
    5. Cem Peker, 2023. "Extracting the collective wisdom in probabilistic judgments," Theory and Decision, Springer, vol. 94(3), pages 467-501, April.
    6. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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