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The Composition of Optimally Wise Crowds

Author

Listed:
  • Clintin P. Davis-Stober

    (Department of Psychological Sciences, University of Missouri, Columbia, Missouri, 65211)

  • David V. Budescu

    (Department of Psychology, Fordham University, Bronx, New York, 10458)

  • Stephen B. Broomell

    (Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213)

  • Jason Dana

    (School of Management, Yale University, New Haven, Connecticut, 06520)

Abstract

We investigate optimal group member configurations for producing a maximally accurate group forecast. Our approach accounts for group members that may be biased in their forecasts and/or have errors that correlate with the criterion values being forecast. We show that for large forecasting groups, the diversity of individual forecasts linearly trades off with forecaster accuracy when determining optimal group composition.

Suggested Citation

  • Clintin P. Davis-Stober & David V. Budescu & Stephen B. Broomell & Jason Dana, 2015. "The Composition of Optimally Wise Crowds," Decision Analysis, INFORMS, vol. 12(3), pages 130-143.
    • Handle: RePEc:inm:ordeca:v:12:y:2015:i:3:p:130-143
    DOI: 10.1287/deca.2015.0315
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Shinitzky, Hilla & Shemesh, Yhonatan & Leiser, David & Gilead, Michael, 2024. "Improving geopolitical forecasts with 100 brains and one computer," International Journal of Forecasting, Elsevier, vol. 40(3), pages 958-970.
    2. Hoda Heidari & Solon Barocas & Jon Kleinberg & Karen Levy, 2023. "Informational Diversity and Affinity Bias in Team Growth Dynamics," Papers 2301.12091, arXiv.org.
    3. Muye Chen & Michel Regenwetter & Clintin P. Davis-Stober, 2021. "Collective Choice May Tell Nothing About Anyone’s Individual Preferences," Decision Analysis, INFORMS, vol. 18(1), pages 1-24, March.
    4. Michael D. Lee & Megan N. Lee, 2017. "The relationship between crowd majority and accuracy for binary decisions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(4), pages 328-343, July.
    5. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.
    6. 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.
    7. Ghezelbash, Ehsan & Yazdanpanah, Mohammad Javad & Asadpour, Masoud, 2019. "Polarization in cooperative networks through optimal placement of informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    8. Asa B. Palley & Ville A. Satopää, 2023. "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions," Management Science, INFORMS, vol. 69(9), pages 5128-5146, September.
    9. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    10. Bonazzi, Riccardo & Viscusi, Gianluigi & Solidoro, Adriano, 2024. "Crowd mining as a strategic resource for innovation seekers," Technovation, Elsevier, vol. 132(C).
    11. Coates, Dennis & Parshakov, Petr, 2022. "The wisdom of crowds and transfer market values," European Journal of Operational Research, Elsevier, vol. 301(2), pages 523-534.
    12. Ming Tang & Huchang Liao, 2023. "Group Structure and Information Distribution on the Emergence of Collective Intelligence," Decision Analysis, INFORMS, vol. 20(2), pages 133-150, June.
    13. Hilla Shinitzky & Dan Avraham & Yizhak Vaisman & Yakir Tsizer & Yaniv Leedon & Yuval Shahar, 2024. "Exploiting Meta-cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation," Group Decision and Negotiation, Springer, vol. 33(1), pages 87-111, February.
    14. Shu Huang & Russell Golman, 2025. "The collective wisdom of behavioral game theory," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 79(1), pages 341-356, February.
    15. David Soule & Yael Grushka-Cockayne & Jason Merrick, 2024. "A Heuristic for Combining Correlated Experts When There Are Few Data," Management Science, INFORMS, vol. 70(10), pages 6637-6668, October.
    16. Ville A. Satopää & Marat Salikhov & Philip E. Tetlock & Barbara Mellers, 2021. "Bias, Information, Noise: The BIN Model of Forecasting," Management Science, INFORMS, vol. 67(12), pages 7599-7618, December.
    17. Kamran-Disfani, Omid & Mantrala, Murali, 2024. "Can crowdsourcing improve prediction accuracy in fashion retail buying?," Journal of Retailing, Elsevier, vol. 100(3), pages 404-421.
    18. Saurabh Bansal & Genaro J. Gutierrez, 2020. "Estimating Uncertainties Using Judgmental Forecasts with Expert Heterogeneity," Operations Research, INFORMS, vol. 68(2), pages 363-380, March.
    19. Jon Atwell & Marlon Twyman II, 2023. "Metawisdom of the Crowd: Experimental Evidence of Crowd Accuracy Through Diverse Choices of Decision Aids," Papers 2308.15451, arXiv.org, revised Dec 2025.

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