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Optimal Forecasting Groups

Author

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  • P. J. Lamberson

    (Kellogg School of Management and Northwestern University Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208)

  • Scott E. Page

    (Center for the Study of Complex Systems, Departments of Economics and Political Science, University of Michigan, Ann Arbor, Michigan 48106)

Abstract

This paper characterizes the optimal composition of a group for making a combined forecast. In the model, individual forecasters have types defined according to a statistical criterion we call type coherence. Members of the same type have identical expected accuracy, and forecasters within a type have higher covariance than forecasters of different types. We derive the optimal group composition as a function of predictive accuracy, between- and within-type covariance, and group size. Group size plays a critical role in determining the optimal group: in small groups the most accurate type should be in the majority, whereas in large groups the type with the least within-type covariance should dominate. This paper was accepted by Peter Wakker, decision analysis.

Suggested Citation

  • P. J. Lamberson & Scott E. Page, 2012. "Optimal Forecasting Groups," Management Science, INFORMS, vol. 58(4), pages 805-810, April.
    • Handle: RePEc:inm:ormnsc:v:58:y:2012:i:4:p:805-810
    DOI: 10.1287/mnsc.1110.1441
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    References listed on IDEAS

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    4. Bernd Frick & Franziska Prockl, 2018. "Information Precision In Online Communities: Player Valuations On Www.Transfermarkt.De," Working Papers Dissertations 37, Paderborn University, Faculty of Business Administration and Economics.
    5. 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.
    6. 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.
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    11. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.
    12. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    13. Peeters, Thomas, 2018. "Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results," International Journal of Forecasting, Elsevier, vol. 34(1), pages 17-29.
    14. Leslie Paul Thiele, 2020. "Integrating political and technological uncertainty into robust climate policy," Climatic Change, Springer, vol. 163(1), pages 521-538, November.
    15. 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.
    16. Joshua Becker & Abdullah Almaatouq & EmH{o}ke-'Agnes Horv'at, 2020. "Network Structures of Collective Intelligence: The Contingent Benefits of Group Discussion," Papers 2009.07202, arXiv.org, revised Mar 2021.
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    18. Förster, Bernadette & von der Gracht, Heiko, 2014. "Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 215-229.

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