Environmental Decisions in the Face of Uncertainty (2013) / Chapter Skim
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Appendix A: Approaches to Accounting for Uncertainty
Appendix A: Approaches to Accounting for Uncertainty
Pages 229-246
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From page 229... ...
As a result, it is necessary to apply a mix of statistical analyses and expert judgments. HEALTH ONLY When assessing human health risks, the main uncertainties arise in projecting exposures and health effects in the baseline case -- that is, absent a change in a risk management strategy -- and in projecting the effects of a given management intervention (for example, a proposed regulatory action, such as the implementation of an emission standard)
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From page 230... ...
. With this happens -- a situation sometimes referred to as compounding conservatism -- the precaution level for each individual analysis might be such that the marginal cost of precaution equals or slightly outweighs the marginal health benefit, but when multiple analyses use that level of precaution and are combined, the precaution level becomes such that the overall marginal cost far exceeds the overall marginal benefit.
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From page 231... ...
For example, a probabilistic risk analysis would quantify the uncertainties about a dose–response relationship for exposure to fine particulates by using epidemiological studies to construct a probability distribution around the slope of the dose–response function. The typical decision rule in a probabilistic risk analysis is to select a standard or regulatory option that satisfies a specific probability criterion.
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From page 232... ...
. The possibility of nonregular probability distributions makes performing a probabilistic risk analysis more difficult, especially when the underlying distribution for a parameter has what is termed a "fat tail." Distributions of extreme events can have fat tails (for discussion, see Farber, 2007)
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From page 233... ...
As a practical matter, however, it may not be possible to distinguish between the two sources of error. MODEL AND PARAMETER UNCERTAINTY Expert elicitations are often used when dealing with uncertainty about what statistical model to use and which parameters to use in the model (second row, first column in Table 5-1)
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From page 234... ...
Disagreements about which model is appropriate for low-dose extrapolations of the cancer risks of dioxin have resulted in extensive delays in finalizing the dioxin health risk assessment. Although expert elicitation might be able to guide the decision of which model is most appropriate, there are only a handful of examples of using expert elicitation processes for this purpose in the environmental setting.
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From page 235... ...
If there is insufficient time, if there is insufficient consensus information for expert elicitation and subjective model weighting or averaging to estimate model uncertainty, or if such uncertainty analyses are not required given the context of the decision, then one can choose to use a structured system of model defaults and criteria for departures from those defaults. Science and Decisions (NRC, 2009)
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From page 236... ...
Expert elicitations and expert judgments can provide much of the needed information concerning technology availability. In answering the first question -- which appropriate technologies are available or soon will be -- a key issue is the uncertainty about the likelihood that a relatively new technology can be successfully deployed and, if it is successfully deployed, how well it will perform.
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From page 237... ...
As with other decision rules, the decision maker is using predicted estimates for the future; the decisions, therefore, are based on estimates of future values. These estimates reflect the underlying probability distributions of potential costs and benefits, and the approaches require an explicit consideration of the underlying 5 Cost–benefit analyses for business applications typically are not made public and are con ducted to provide information to maximize profits.
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From page 238... ...
outcomes, which is a health-protective approach. The decision rule in multiattribute utility analysis is to select the regulatory option that maximizes expected utility.
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. A number of characteristics of multiattribute utility analysis make it useful to environmental decision makers.
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From page 240... ...
. Adaptive management strategies characterize uncertainty by using multiple representations of the future rather than a single set of probability distributions, as in optimum expected utility analysis (Lempert and Collins, 2007)
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From page 241... ...
Some departments, such as the Department of Defense, have moved away from examining the worst-case scenario and focus instead on the more likely scenarios. By examining a number of different scenarios in human health risk assessments, including scenarios using defaults, EPA could examine the effects of the different scenarios, and risk management decision makers could choose the scenario that produces their desired level of precaution for the decision context.
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From page 242... ...
There is no operational definition for when a lack of consensus about the appropriate models for a particular decision-making problem becomes a case of deep uncertainty in which robust decision-making tools would be helpful. A solution that may work in some cases would be to use both traditional decision-making analysis techniques (such as expected utility)
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From page 243... ...
2009. Elicitation by design in ecology: Using expert opinion to inform priors for Bayesian statistical models.
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2005. Application of mul ticriteria decision analysis in environmental decision making.
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1987. A multiattribute utility analysis of alternative sites for the disposal of nuclear waste.
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From page 246... ...
2008. Expert elicitation of recharge model proba bilities for the Death Valley regional flow system.
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