On the need for restricting the probabilistic analysis in risk assessments to variability

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.