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Confidence Calibration in a Multiyear Geopolitical Forecasting Competition

Author

Listed:
  • Don A. Moore

    (University of California, Berkeley, Berkeley, California 94720)

  • Samuel A. Swift

    (Betterment, LLC, New York, New York 10010)

  • Angela Minster

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Barbara Mellers

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Lyle Ungar

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Philip Tetlock

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Heather H. J. Yang

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Elizabeth R. Tenney

    (University of Utah, Salt Lake City, Utah 84112)

Abstract

This research examines the development of confidence and accuracy over time in the context of forecasting. Although overconfidence has been studied in many contexts, little research examines its progression over long periods of time or in consequential policy domains. This study employs a unique data set from a geopolitical forecasting tournament spanning three years in which thousands of forecasters predicted the outcomes of hundreds of events. We sought to apply insights from research to structure the questions, interactions, and elicitations to improve forecasts. Indeed, forecasters’ confidence roughly matched their accuracy. As information came in, accuracy increased. Confidence increased at approximately the same rate as accuracy, and good calibration persisted. Nevertheless, there was evidence of a small amount of overconfidence (3%), especially on the most confident forecasts. Training helped reduce overconfidence, and team collaboration improved forecast accuracy. Together, teams and training reduced overconfidence to 1%. Our results provide reason for tempered optimism regarding confidence calibration and its development over time in consequential field contexts.

Suggested Citation

  • Don A. Moore & Samuel A. Swift & Angela Minster & Barbara Mellers & Lyle Ungar & Philip Tetlock & Heather H. J. Yang & Elizabeth R. Tenney, 2017. "Confidence Calibration in a Multiyear Geopolitical Forecasting Competition," Management Science, INFORMS, vol. 63(11), pages 3552-3565, November.
  • Handle: RePEc:inm:ormnsc:v:63:y:2017:i:11:p:3552-3565
    DOI: 10.1287/mnsc.2016.2525
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    References listed on IDEAS

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    3. Ross Gruetzemacher & Kang Bok Lee & David Paradice, 2024. "Calibration training for improving probabilistic judgments using an interactive app," Futures & Foresight Science, John Wiley & Sons, vol. 6(2), June.
    4. Karvetski, Christopher W. & Meinel, Carolyn & Maxwell, Daniel T. & Lu, Yunzi & Mellers, Barbara A. & Tetlock, Philip E., 2022. "What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 688-704.
    5. Astebro, Thomas B. & Fossen, Frank M. & Gutierrez, Cédric, 2024. "Entrepreneurs: Clueless, Biased, Poor Heuristics, or Bayesian Machines?," IZA Discussion Papers 17231, Institute of Labor Economics (IZA).

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