Decision support models help structure and inform complex choices under uncertainty. Two classic models are risk analysis and decision analysis. Risk analysis is understood here as risk characterization, and in some cases, the identification and benefit assessment of some risk management options. It is based on systems analysis and probability, and it excludes the actual decision phase, which requires the preferences, e.g., the utility function, of the decision maker(s). Risk analysis and decision analysis have some similarities and are often complementary. To model uncertainties, both rely on probability, generally a subjective Bayesian degree of belief. A decision analysis can include a risk analysis component, and the design of a risk management plan may require decision analysis support. The challenge for risk analysts is to characterize potential failure problems before decision options have been identified, and when there is no single decision maker, or group of decision makers, who can provide preference functions and degrees of belief. Yet, a correct and complete model of uncertainties in the probabilistic risk analysis phase is important if the results are to be used later for decision support, especially when the number of systems involved and the duration of their operations is unknown. In this paper, we explore some of the challenges inherent to probabilistic risk analysis that should be of interest to the decision analysts who intend to use risk analysis results.
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