Risk reduction prioritization using decision analysis

Abstract

The ALARP principle is applied in many areas to regulate the tolerable level of risk. Usually the principle is operationalized by assigning a value per fatality. A cost-benefit analysis is used to trade the expected value of lives saved with the costs of technical measures required to reduce risks. In sectors in which risks have been reduced over a period of years, it is difficult to pinpoint those areas in which further risk reduction might be sought. In this article we show that many different risk reduction mechanisms can be considered simultaneously in a decision analysis framework. Using influence diagrams it is straightforward to build mini-decision analysis models in which competing alternatives addressing the same risk can be compared. The mini-model decision alternatives are assembled into decision strategies representing the best possible combination of alternatives at different cost/benefit ratios. Disynergies between the different alternatives are highlighted through the model. The overall aim is to build a high-level model to explore the sensitivity of risk reduction measures to the value per fatality parameter. This enables decision makers to gain a better understanding of the cost of measures required to obtain a global reduction in risk.