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Individual and Group Biases in Value and Uncertainty Judgments

In: Elicitation

Author

Listed:
  • Gilberto Montibeller

    (Loughborough University)

  • Detlof Winterfeldt

    (University of Southern California)

Abstract

Behavioral decision research has demonstrated that value and uncertainty judgments of decision makers and experts are subject to numerous biases. Individual biases can be either cognitive, such as overconfidence, or motivational, such as wishful thinking. In addition, when making judgements in groups, decision makers and experts might be affected by group-level biases. These biases can create serious challenges to decision analysts, who need judgments as inputs to a decision or risk analysis model, because they can degrade the quality of the analysis. This chapter identifies individual and group biases relevant for decision and risk analysis and suggests tools for debiasing judgements for each type of bias.

Suggested Citation

  • Gilberto Montibeller & Detlof Winterfeldt, 2018. "Individual and Group Biases in Value and Uncertainty Judgments," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 377-392, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65052-4_15
    DOI: 10.1007/978-3-319-65052-4_15
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    Citations

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    Cited by:

    1. Luis C. Dias & Gabriela D. Oliveira & Paula Sarabando, 2021. "Choice-based preference disaggregation concerning vehicle technologies," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 177-200, March.
    2. Ashraf Labib & Salem Chakhar & Lorraine Hope & John Shimell & Mark Malinowski, 2022. "Analysis of noise and bias errors in intelligence information systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1755-1775, December.
    3. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    4. Kuller, M. & Beutler, P. & Lienert, J., 2023. "Preference change in stakeholder group-decision processes in the public sector: Extent, causes and implications," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1268-1285.
    5. Elna Schirrmeister & Anne‐Louise Göhring & Philine Warnke, 2020. "Psychological biases and heuristics in the context of foresight and scenario processes," Futures & Foresight Science, John Wiley & Sons, vol. 2(2), June.
    6. Gilberto Montibeller & L. Alberto Franco & Ashley Carreras, 2020. "A Risk Analysis Framework for Prioritizing and Managing Biosecurity Threats," Risk Analysis, John Wiley & Sons, vol. 40(11), pages 2462-2477, November.
    7. Niels Bugert & Rainer Lasch, 2023. "Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks," Journal of Business Economics, Springer, vol. 93(5), pages 859-889, July.
    8. Elisa F. Long & Gilberto Montibeller & Jun Zhuang, 2022. "Health Decision Analysis: Evolution, Trends, and Emerging Topics," Decision Analysis, INFORMS, vol. 19(4), pages 255-264, December.
    9. Haag, Fridolin & Zürcher, Sara & Lienert, Judit, 2019. "Enhancing the elicitation of diverse decision objectives for public planning," European Journal of Operational Research, Elsevier, vol. 279(3), pages 912-928.

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