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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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    1. Sinclair, Tara M. & Joutz, Fred & Stekler, H.O., 2010. "Can the Fed predict the state of the economy?," Economics Letters, Elsevier, vol. 108(1), pages 28-32, July.
    2. David V. Budescu & Eva Chen, 2015. "Identifying Expertise to Extract the Wisdom of Crowds," Management Science, INFORMS, vol. 61(2), pages 267-280, February.
    3. Joseph P. Simmons & Leif D. Nelson & Jeff Galak & Shane Frederick, 2011. "Intuitive Biases in Choice versus Estimation: Implications for the Wisdom of Crowds," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 38(1), pages 1-15.
    4. Clintin Davis-Stober & Jason Dana & David Budescu, 2010. "A Constrained Linear Estimator for Multiple Regression," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 521-541, September.
    5. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    6. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    7. Victor Richmond R. Jose & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "Trimmed Opinion Pools and the Crowd's Calibration Problem," Management Science, INFORMS, vol. 60(2), pages 463-475, February.
    8. P. J. Lamberson & Scott E. Page, 2012. "Optimal Forecasting Groups," Management Science, INFORMS, vol. 58(4), pages 805-810, April.
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    Citations

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    Cited by:

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    2. 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.
    3. 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.
    4. Hoda Heidari & Solon Barocas & Jon Kleinberg & Karen Levy, 2023. "Informational Diversity and Affinity Bias in Team Growth Dynamics," Papers 2301.12091, arXiv.org.
    5. 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.
    6. 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.
    7. repec:cup:judgdm:v:12:y:2017:i:4:p:328-343 is not listed on IDEAS
    8. 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.
    9. 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.
    10. Saurabh Bansal & Genaro J. Gutierrez, 2020. "Estimating Uncertainties Using Judgmental Forecasts with Expert Heterogeneity," Operations Research, INFORMS, vol. 68(2), pages 363-380, March.
    11. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.
    12. 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.
    13. Jon Atwell & Marlon Twyman II, 2023. "Metawisdom of the Crowd: How Choice Within Aided Decision Making Can Make Crowd Wisdom Robust," Papers 2308.15451, arXiv.org.

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