Quantifying uncertainty under a predictive, epistemic approach to risk analysis

Risk analysis is a tool for investigating and reducing uncertainty related to outcomes of future activities. Probabilities are key elements in risk analysis, but confusion about interpretation and use of probabilities often weakens the message from the analyses. Under the predictive, epistemic approach to risk analysis, probabilities are used to express uncertainty related to future values of observable quantities like the number of fatalities or monetary loss in a period of time. The procedure for quantifying this uncertainty in terms of probabilities is, however, not obvious. Examples of topics from the literature relevant in this discussion are use of expert judgement, the effect of so-called heuristics and biases, application of historical data, dependency and updating of probabilities. The purpose of this paper is to discuss and give guidelines on how to quantify uncertainty in the perspective of these topics. Emphasis is on the use of models and assessment of uncertainties of similar quantities.