IDEAS home Printed from https://ideas.repec.org/a/eee/jobhdp/v131y2015icp110-120.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0749597815000989
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.obhdp.2015.09.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. repec:cup:judgdm:v:12:y:2017:i:1:p:29-41 is not listed on IDEAS
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Jiang, Li & Hao, Zhongyuan, 2024. "Holding diverse market beliefs by firms: Information flow, profit performances, and channel structure," Omega, Elsevier, vol. 126(C).
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sulian Wang & Chen Wang, 2021. "Quantile Judgments of Lognormal Losses: An Experimental Investigation," Decision Analysis, INFORMS, vol. 18(1), pages 78-99, March.
    2. 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.
    3. Becchetti, Leonardo & Degli Antoni, Giacomo & Ottone, Stefania & Solferino, Nazaria, 2013. "Allocation criteria under task performance: The gendered preference for protection," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 45(C), pages 96-111.
    4. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    5. Dan Zhu & Qingwei Wang & John Goddard, 2022. "A new hedging hypothesis regarding prediction interval formation in stock price forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 697-717, July.
    6. Benjamin Enke & Florian Zimmermann, 2019. "Correlation Neglect in Belief Formation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 313-332.
    7. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    8. George Deltas & Richard Engelbrecht-Wiggans, 2005. "Naive Bidding," Management Science, INFORMS, vol. 51(3), pages 328-338, March.
    9. Schultze, Thomas & Schulz-Hardt, Stefan, 2015. "The impact of biased information and corresponding meta-information on escalating commitment," Journal of Economic Psychology, Elsevier, vol. 49(C), pages 108-119.
    10. Manuel Gebetsberger & Jakob W. Messner & Georg J. Mayr & Achim Zeileis, 2017. "Estimation methods for non-homogeneous regression models: Minimum continuous ranked probability score vs. maximum likelihood," Working Papers 2017-23, Faculty of Economics and Statistics, Universität Innsbruck.
    11. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    12. Patrick Krieger & Carsten Lausberg, 2021. "Entscheidungen, Entscheidungsfindung und Entscheidungsunterstützung in der Immobilienwirtschaft: Eine systematische Literaturübersicht [Decisions, decision-making and decisions support systems in r," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 7(1), pages 1-33, April.
    13. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    14. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
    15. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    16. Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
    17. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    18. Bryce McLaughlin & Jann Spiess, 2022. "Algorithmic Assistance with Recommendation-Dependent Preferences," Papers 2208.07626, arXiv.org, revised Jan 2024.
    19. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
    20. Markus Glaser & Thomas Langer & Martin Weber, 2007. "On the Trend Recognition and Forecasting Ability of Professional Traders," Decision Analysis, INFORMS, vol. 4(4), pages 176-193, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jobhdp:v:131:y:2015:i:c:p:110-120. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/obhdp .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.