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Risk‐Based Environmental Remediation: Bayesian Monte Carlo Analysis and the Expected Value of Sample Information

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  • Maxine E. Dakins
  • John E. Toll
  • Mitchell J. Small
  • Kevin P. Brand

Abstract

A methodology that simulates outcomes from future data collection programs, utilizes Bayesian Monte Carlo analysis to predict the resulting reduction in uncertainty in an environmental fate‐and‐transport model, and estimates the expected value of this reduction in uncertainty to a risk‐based environmental remediation decision is illustrated considering polychlorinated biphenyl (PCB) sediment contamination and uptake by winter flounder in New Bedford Harbor, MA. The expected value of sample information (EVSI), the difference between the expected loss of the optimal decision based on the prior uncertainty analysis and the expected loss of the optimal decision from an updated information state, is calculated for several sampling plan. For the illustrative application we have posed, the EVSI for a sampling plan of two data points is $9.4 million, for five data points is $10.4 million, and for ten data points is $11.5 million. The EVSI for sampling plans involving larger numbers of data points is bounded by the expected value of perfect information, $15.6 million. A sensitivity analysis is conducted to examine the effect of selected model structure and parametric assumptions on the optimal decision and the EVSI. The optimal decision (total area to be dredged) is sensitive to the assumption of linearity between PCB sediment concentration and flounder PCB body burden and to the assumed relationship between area dredged and the harbor‐wide average sediment PCB concentration; these assumptions also have a moderate impact on the computed EVSI. The EVSI is most sensitive to the unit cost of remediation and rather insensitive to the penalty cost associated with under‐remediation.

Suggested Citation

  • Maxine E. Dakins & John E. Toll & Mitchell J. Small & Kevin P. Brand, 1996. "Risk‐Based Environmental Remediation: Bayesian Monte Carlo Analysis and the Expected Value of Sample Information," Risk Analysis, John Wiley & Sons, vol. 16(1), pages 67-79, February.
    • Handle: RePEc:wly:riskan:v:16:y:1996:i:1:p:67-79
    DOI: 10.1111/j.1539-6924.1996.tb01437.x
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    References listed on IDEAS

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    1. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    2. Kan Shao & Mitchell J. Small, 2011. "Potential Uncertainty Reduction in Model‐Averaged Benchmark Dose Estimates Informed by an Additional Dose Study," Risk Analysis, John Wiley & Sons, vol. 31(10), pages 1561-1575, October.
    3. Fumie Yokota & Kimberly M. Thompson, 2004. "Value of Information Literature Analysis: A Review of Applications in Health Risk Management," Medical Decision Making, , vol. 24(3), pages 287-298, June.
    4. Byron K Williams & Fred A Johnson, 2018. "Value of sample information in dynamic, structurally uncertain resource systems," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    5. Bjørnsen, Kjartan & Selvik, Jon Tømmerås & Aven, Terje, 2019. "A semi-quantitative assessment process for improved use of the expected value of information measure in safety management," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 494-502.
    6. A. E. Ades & S. Cliffe, 2002. "Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty," Medical Decision Making, , vol. 22(4), pages 359-371, August.
    7. John D. Graham, 2001. "Decision-analytic refinements of the precautionary principle," Journal of Risk Research, Taylor & Francis Journals, vol. 4(2), pages 127-141, April.
    8. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.

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