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Measures of Compounding Conservatism in Probabilistic Risk Assessment

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  • Alison C. Cullen

Abstract

Concern about the degree of uncertainty and potential conservatism in deterministic point estimates of risk has prompted researchers to turn increasingly to probabilistic methods for risk assessment. With Monte Carlo simulation techniques, distributions of risk reflecting uncertainty and/or variability are generated as an alternative. In this paper the compounding of conservatism(1) between the level associated with point estimate inputs selected from probability distributions and the level associated with the deterministic value of risk calculated using these inputs is explored. Two measures of compounded conservatism are compared and contrasted. The first measure considered, F, is defined as the ratio of the risk value, Rd, calculated deterministically as a function of n inputs each at the jth percentile of its probability distribution, and the risk value, Rj that falls at the jth percentile of the simulated risk distribution (i.e., F=Rd/Rj). The percentile of the simulated risk distribution which corresponds to the deterministic value, Rd, serves as a second measure of compounded conservatism. Analytical results for simple products of lognormal distributions are presented. In addition, a numerical treatment of several complex cases is presented using five simulation analyses from the literature to illustrate. Overall, there are cases in which conservatism compounds dramatically for deterministic point estimates of risk constructed from upper percentiles of input parameters, as well as those for which the effect is less notable. The analytical and numerical techniques discussed are intended to help analysts explore the factors that influence the magnitude of compounding conservatism in specific cases.

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  • Alison C. Cullen, 1994. "Measures of Compounding Conservatism in Probabilistic Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 389-393, August.
    • Handle: RePEc:wly:riskan:v:14:y:1994:i:4:p:389-393
    DOI: 10.1111/j.1539-6924.1994.tb00257.x
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    1. Kimberly M. Thompson & David E. Burmaster & Edmund A.C. Crouch3, 1992. "Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 53-63, March.
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    1. Jane G. Pouzou & Alison C. Cullen & Michael G. Yost & John C. Kissel & Richard A. Fenske, 2018. "Comparative Probabilistic Assessment of Occupational Pesticide Exposures Based on Regulatory Assessments," Risk Analysis, John Wiley & Sons, vol. 38(6), pages 1223-1238, June.
    2. Katherine E. Von Stackelberg & Dmitriy Burmistrov & Donna J. Vorhees & Todd Bridges & Igor Linkov, 2002. "Importance of Uncertainty and Variability to Predicted Risks from Trophic Transfer of PCBs in Dredged Sediments," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 499-512, June.
    3. Viscusi, W. Kip & Hamilton, James T. & Dockins, P. Christen, 1997. "Conservative versus Mean Risk Assessments: Implications for Superfund Policies," Journal of Environmental Economics and Management, Elsevier, vol. 34(3), pages 187-206, November.
    4. Paul S. Price & Steave H. Su & Jeff R. Harrington & Russell E. Keenan, 1996. "Uncertainty and Variation in Indirect Exposure Assessments: An Analysis of Exposure to Tetrachlorodibenzo‐p‐Dioxin from a Beef Consumption Pathway," Risk Analysis, John Wiley & Sons, vol. 16(2), pages 263-277, April.

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