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Wide of the mark: Evidence on the underlying causes of overprecision in judgment

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  • Moore, Don A.
  • Carter, Ashli B.
  • Yang, Heather H.J.

Abstract

Overprecision is the most robust and least understood form of overconfidence. In an attempt to elucidate the underlying causes of overprecision in judgment, the present paper offers a new approach – examining people’s beliefs about the likelihood of chance events drawn from known probability distributions. This approach allows us to test the assumption that low hit rates inside subjective confidence intervals arise because those confidence intervals are drawn too narrowly. In fact, subjective probability distributions are systematically too wide, or insufficiently precise. This result raises profound questions for the study of overconfidence.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jobhdp:v:131:y:2015:i:c:p:110-120
    DOI: 10.1016/j.obhdp.2015.09.003
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    References listed on IDEAS

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    1. 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.
    2. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    3. repec:cup:judgdm:v:5:y:2010:i:7:p:467-476 is not listed on IDEAS
    4. Block, Richard A. & Harper, David R., 1991. "Overconfidence in estimation: Testing the anchoring-and-adjustment hypothesis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 49(2), pages 188-207, August.
    5. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
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    Citations

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    Cited by:

    1. Jack B. Soll & Asa B. Palley & Joshua Klayman & Don A. Moore, 2024. "Overconfidence in Probability Distributions: People Know They Don’t Know, but They Don’t Know What to Do About It," Management Science, INFORMS, vol. 70(11), pages 7422-7442, November.
    2. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    3. repec:cup:judgdm:v:12:y:2017:i:1:p:29-41 is not listed on IDEAS
    4. Michael Muthukrishna & Joseph Henrich & Wataru Toyokawa & Takeshi Hamamura & Tatsuya Kameda & Steven J Heine, 2018. "Overconfidence is universal? Elicitation of Genuine Overconfidence (EGO) procedure reveals systematic differences across domain, task knowledge, and incentives in four populations," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-30, August.
    5. 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.
    6. Goodwin, Paul & Gönül, M. Sinan & Önkal, Dilek, 2019. "When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions," European Journal of Operational Research, Elsevier, vol. 273(3), pages 992-1004.
    7. Camilleri, Adrian R. & Newell, Ben R., 2019. "Better calibration when predicting from experience (rather than description)," Organizational Behavior and Human Decision Processes, Elsevier, vol. 150(C), pages 62-82.
    8. Hao, Zhongyuan & Li, Juan & Cai, Jinling, 2023. "Allocation of inventory responsibilities in overconfident supply chains," European Journal of Operational Research, Elsevier, vol. 305(1), pages 207-221.
    9. López-Pérez, Raúl & Rodriguez-Moral, Antonio & Vorsatz, Marc, 2021. "Simplified mental representations as a cause of overprecision," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 92(C).
    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," European Journal of Operational Research, Elsevier, vol. 304(2), pages 661-675.
    11. Jiang, Li & Hao, Zhongyuan, 2024. "Holding diverse market beliefs by firms: Information flow, profit performances, and channel structure," Omega, Elsevier, vol. 126(C).
    12. Julia P. Prims & Don A. Moore, 2017. "Overconfidence over the lifespan," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(1), pages 29-41, January.

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