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Expert Judgment in Risk Analysis and Management: Process, Context, and Pitfalls

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  • Harry Otway
  • Detlof von Winterfeldt

Abstract

The regulation and management of hazardous industrial activities increasingly rely on formal expert judgment processes to provide wisdom in areas of science and technology where traditional “good science” is, in practice, unable to supply unambiguous “facts.” Expert judgment has always played a significant, if often unrecognized, role in analysis; however, recent trends are to make it formal, explicit, and documented so it can be identified and reviewed by others. We propose four categories of expert judgment and present three case studies which illustrate some of the pitfalls commonly encountered in its use. We conclude that there will be an expanding policy role for formal expert judgment and that the openness, transparency, and documentation that it requires have implications for enhanced public involvement in scientific and technical affairs.

Suggested Citation

  • Harry Otway & Detlof von Winterfeldt, 1992. "Expert Judgment in Risk Analysis and Management: Process, Context, and Pitfalls," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 83-93, March.
    • Handle: RePEc:wly:riskan:v:12:y:1992:i:1:p:83-93
    DOI: 10.1111/j.1539-6924.1992.tb01310.x
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    13. Roland W. Scholz & Yann B. Blumer & Fridolin S. Brand, 2012. "Risk, vulnerability, robustness, and resilience from a decision-theoretic perspective," Journal of Risk Research, Taylor & Francis Journals, vol. 15(3), pages 313-330, March.
    14. George M. Gray & Jon C. Allen & David E. Burmaster & Stuart H. Gage & James K. Hammitt & Stanley Kaplan & Ralph L. Keeney & Joseph G. Morse & D. Warner North & Jan P. Nyrop & Alina Stahevitch & Richar, 1998. "Principles for Conduct of Pest Risk Analyses: Report of an Expert Work hop," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 773-780, December.
    15. Brito, Mario & Griffiths, Gwyn, 2016. "A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 55-67.
    16. Luca Podofillini & Vinh Dang & Enrico Zio & Piero Baraldi & Massimo Librizzi, 2010. "Using Expert Models in Human Reliability Analysis—A Dependence Assessment Method Based on Fuzzy Logic," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1277-1297, August.
    17. Gillenwater, Michael, 2013. "Probabilistic decision model of wind power investment and influence of green power market," Energy Policy, Elsevier, vol. 63(C), pages 1111-1125.
    18. Sjöberg, Lennart, 2003. "Risk communication between experts and the public: perceptions and intentions," SSE/EFI Working Paper Series in Business Administration 2003:13, Stockholm School of Economics.
    19. Ine H. J. Van Der Fels‐Klerx & Louis H. J. Goossens & Helmut W. Saatkamp & Suzan H. S. Horst, 2002. "Elicitation of Quantitative Data from a Heterogeneous Expert Panel: Formal Process and Application in Animal Health," Risk Analysis, John Wiley & Sons, vol. 22(1), pages 67-81, February.
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    21. John S. Evans & John D. Graham & George M. Gray & Robert L. Sielken, 1994. "A Distributional Approach to Characterizing Low‐Dose Cancer Risk," Risk Analysis, John Wiley & Sons, vol. 14(1), pages 25-34, February.
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    24. Justin M. Ross & Sarah E. Larson & Chad Wall, 2012. "Are Surveys Of Experts Unbiased? Evidence From College Football Rankings," Contemporary Economic Policy, Western Economic Association International, vol. 30(4), pages 502-522, October.
    25. Mostafa Aliyari & Yonas Z Ayele & Abbas Barabadi & Enrique Lopez Droguett, 2019. "Risk analysis in low-voltage distribution systems," Journal of Risk and Reliability, , vol. 233(2), pages 118-138, April.

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