Deciding with thresholds: importance measures and value of information

Risk-informed decision-making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value-of-information based importance measure that brings together the risk metric, risk importance measures and the risk threshold in one expression. The new importance measure does not impose additional computational burden, because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large LOCA sequence of the ATR reactor helps us in illustrating the risk analysis insights.