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On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability

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  • Terje Aven
Abstract
It is common perspective in risk analysis that there are two kinds of uncertainties: i) variability as resulting from heterogeneity and stochasticity (aleatory uncertainty) and ii) partial ignorance or epistemic uncertainties resulting from systematic measurement error and lack of knowledge. Probability theory is recognized as the proper tool for treating the aleatory uncertainties, but there are different views on what is the best approach for describing partial ignorance and epistemic uncertainties. Subjective probabilities are often used for representing this type of ignorance and uncertainties, but several alternative approaches have been suggested, including interval analysis, probability bound analysis, and bounds based on evidence theory. It is argued that probability theory generates too precise results when the background knowledge of the probabilities is poor. In this article, we look more closely into this issue. We argue that this critique of probability theory is based on a conception of risk assessment being a tool to objectively report on the true risk and variabilities. If risk assessment is seen instead as a method for describing the analysts’ (and possibly other stakeholders’) uncertainties about unknown quantities, the alternative approaches (such as the interval analysis) often fail in providing the necessary decision support.

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  • Terje Aven, 2010. "On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 354-360, March.
    • Handle: RePEc:wly:riskan:v:30:y:2010:i:3:p:354-360
    DOI: 10.1111/j.1539-6924.2009.01314.x
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    References listed on IDEAS

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    Cited by:

    1. William A. Huber, 2010. "Ignorance Is Not Probability," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 371-376, March.
    2. Isadora Antoniano‐Villalobos & Emanuele Borgonovo & Sumeda Siriwardena, 2018. "Which Parameters Are Important? Differential Importance Under Uncertainty," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2459-2477, November.
    3. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    4. Yu, Xuchao & Liang, Wei & Zhang, Laibin & Reniers, Genserik & Lu, Linlin, 2018. "Risk assessment of the maintenance process for onshore oil and gas transmission pipelines under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 50-67.
    5. Junrui Xu & James H. Lambert, 2015. "Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 624-641, April.
    6. Tim Bedford & Alireza Daneshkhah & Kevin J. Wilson, 2016. "Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 792-815, April.
    7. Didier Dubois, 2010. "Representation, Propagation, and Decision Issues in Risk Analysis Under Incomplete Probabilistic Information," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 361-368, March.
    8. Anil Markandya & Enrica Cian & Laurent Drouet & Josué M. Polanco-Martínez & Francesco Bosello, 2019. "Building Risk into the Mitigation/Adaptation Decisions simulated by Integrated Assessment Models," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(4), pages 1687-1721, December.
    9. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    10. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A critical discussion and practical recommendations on some issues relevant to the non-probabilistic treatment of uncertainty in engineering risk assessment," Post-Print hal-01652230, HAL.
    11. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    12. Nicola Pedroni & Enrico Zio, 2013. "Uncertainty Analysis in Fault Tree Models with Dependent Basic Events," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1146-1173, June.
    13. Christopher W. Karvetski & James H. Lambert, 2012. "Evaluating deep uncertainties in strategic priority‐setting with an application to facility energy investments," Systems Engineering, John Wiley & Sons, vol. 15(4), pages 483-493, December.
    14. Terje Aven, 2010. "Reply to Discussants on “The Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability”," Risk Analysis, John Wiley & Sons, vol. 30(3), pages 381-384, March.
    15. Sarat Sivaprasad & Cameron A. MacKenzie, 2018. "The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility," Decision Analysis, INFORMS, vol. 15(3), pages 139-153, September.
    16. D. Warner North, 2011. "Uncertainties, Precaution, and Science: Focus on the State of Knowledge and How It May Change," Risk Analysis, John Wiley & Sons, vol. 31(10), pages 1526-1529, October.
    17. Suo, Weilan & Wang, Lin & Li, Jianping, 2021. "Probabilistic risk assessment for interdependent critical infrastructures: A scenario-driven dynamic stochastic model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    18. R. Gerrard & A. Tsanakas, 2011. "Failure Probability Under Parameter Uncertainty," Risk Analysis, John Wiley & Sons, vol. 31(5), pages 727-744, May.
    19. Nicola Pedroni & Enrico Zio & Alberto Pasanisi & Mathieu Couplet, 2017. "A Critical Discussion and Practical Recommendations on Some Issues Relevant to the Nonprobabilistic Treatment of Uncertainty in Engineering Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(7), pages 1315-1340, July.
    20. Pengfei Wei & Zhenzhou Lu & Jingwen Song, 2014. "Moment‐Independent Sensitivity Analysis Using Copula," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 210-222, February.

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