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Validating the Contribution-Weighted Model: Robustness and Cost-Benefit Analyses

Author

Listed:
  • Eva Chen

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • David V. Budescu

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

  • Shrinidhi K. Lakshmikanth

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Barbara A. Mellers

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Philip E. Tetlock

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We use results from a multiyear, geopolitical forecasting tournament to highlight the ability of the contribution weighted model [Budescu DV, Chen E (2015) Identifying expertise to extract the wisdom of crowds. Management Sci. 61(2):267–280] to capture and exploit expertise. We show that the model performs better when judges gain expertise from manipulations such as training in probabilistic reasoning and collaborative interaction from serving on teams. We document the model’s robustness using probability judgments from early, middle, and late phases of the forecasting period and by showing its strong performance in the presence of hypothetical malevolent forecasters. The model is highly cost-effective: it operates well, even with random attrition, as the number of judges shrinks and information on their past performance is reduced.

Suggested Citation

  • Eva Chen & David V. Budescu & Shrinidhi K. Lakshmikanth & Barbara A. Mellers & Philip E. Tetlock, 2016. "Validating the Contribution-Weighted Model: Robustness and Cost-Benefit Analyses," Decision Analysis, INFORMS, vol. 13(2), pages 128-152, June.
    • Handle: RePEc:inm:ordeca:v:13:y:2016:i:2:p:128-152
    DOI: 10.1287/deca.2016.0329
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    Cited by:

    1. repec:cup:judgdm:v:14:y:2019:i:4:p:395-411 is not listed on IDEAS
    2. David R. Mandel & Daniel Irwin, 2021. "Tracking accuracy of strategic intelligence forecasts: Findings from a long‐term Canadian study," Futures & Foresight Science, John Wiley & Sons, vol. 3(3-4), September.
    3. 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.
    4. Yuyu Fan & David V. Budescu & David Mandel & Mark Himmelstein, 2019. "Improving Accuracy by Coherence Weighting of Direct and Ratio Probability Judgments," Decision Analysis, INFORMS, vol. 16(3), pages 197-217, September.
    5. 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.
    6. Ying Han & David Budescu, 2019. "A universal method for evaluating the quality of aggregators," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 395-411, July.

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