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Unpacking the Future: A Nudge Toward Wider Subjective Confidence Intervals

Author

Listed:
  • Kriti Jain

    (Decision Sciences, INSEAD, Singapore 138676)

  • Kanchan Mukherjee

    (Indian Institute of Management Bangalore Bangalore 560076, India)

  • J. Neil Bearden

    (Decision Sciences, INSEAD, Singapore 138676)

  • Anil Gaba

    (Decision Sciences, INSEAD, Singapore 138676)

Abstract

Subjective probabilistic judgments in forecasting are inevitable in many real-life domains. A common way to obtain such judgments is to assess fractiles or confidence intervals. However, these judgments tend to be systematically overconfident. Further, it has proved particularly difficult to debias such forecasts and improve the calibration. This paper proposes a simple process that systematically leads to wider confidence intervals, thus reducing overconfidence. With a series of experiments, including with professionals, we show that unpacking the distal future into intermediate more proximal futures systematically improves calibration. We refer to this phenomenon as the time unpacking effect, find it is robust to different elicitation formats, and address the possible reasons behind it. We further show that this results in better overall forecasting performance when improved calibration is traded off against less sharpness, and that substantive benefits can be obtained even from just one level of time unpacking. This paper was accepted by Teck Ho, decision analysis.

Suggested Citation

  • Kriti Jain & Kanchan Mukherjee & J. Neil Bearden & Anil Gaba, 2013. "Unpacking the Future: A Nudge Toward Wider Subjective Confidence Intervals," Management Science, INFORMS, vol. 59(9), pages 1970-1987, September.
  • Handle: RePEc:inm:ormnsc:v:59:y:2013:i:9:p:1970-1987
    DOI: 10.1287/mnsc.1120.1696
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    2. Te Bao & Brice Corgnet & Nobuyuki Hanaki & Katsuhiko Okada & Yohanes E. Riyanto & Jiahua Zhu, 2022. "Financial Forecasting in the Lab and the Field: Qualified Professionals vs. Smart Students," ISER Discussion Paper 1156r, Institute of Social and Economic Research, Osaka University, revised Sep 2024.
    3. Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "Evaluating quantile forecasts in the M5 uncertainty competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1531-1545.
    4. Saemi Park & David V. Budescu, 2015. "Aggregating multiple probability intervals to improve calibration," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(2), pages 130-143, March.
    5. Anil Gaba & Ilia Tsetlin & Robert L. Winkler, 2017. "Combining Interval Forecasts," Decision Analysis, INFORMS, vol. 14(1), pages 1-20, March.
    6. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    7. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    8. Paolo Crosetto & Thomas De Haan, 2022. "Comparing input interfaces to elicit belief distributions," Working Papers 2022-01, Grenoble Applied Economics Laboratory (GAEL).
    9. Huisman, Ronald & Van der Sar, Nico L. & Zwinkels, Remco C.J., 2021. "Volatility expectations and disagreement," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 379-393.
    10. Ferretti, Valentina & Montibeller, Gilberto & von Winterfeldt, Detlof, 2023. "Testing the effectiveness of debiasing techniques to reduce overprecision in the elicitation of subjective continuous probability distributions," LSE Research Online Documents on Economics 115333, London School of Economics and Political Science, LSE Library.
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    12. Steffen Keck & Wenjie Tang, 2018. "Gender Composition and Group Confidence Judgment: The Perils of All-Male Groups," Management Science, INFORMS, vol. 64(12), pages 5877-5898, December.
    13. Moore, Don A. & Carter, Ashli B. & Yang, Heather H.J., 2015. "Wide of the mark: Evidence on the underlying causes of overprecision in judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 131(C), pages 110-120.
    14. Ferretti, Valentina & Montibeller, Gilberto & von Winterfeldt, Detlof, 2023. "Testing the effectiveness of debiasing techniques to reduce overprecision in the elicitation of subjective continuous probability distributions," European Journal of Operational Research, Elsevier, vol. 304(2), pages 661-675.
    15. Sulian Wang & Chen Wang, 2021. "Quantile Judgments of Lognormal Losses: An Experimental Investigation," Decision Analysis, INFORMS, vol. 18(1), pages 78-99, March.

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