A multi-dimensional approach for analyzing risk-related decision problems to enhance decision making and prevent accidents

Abstract

Risk-related decision problems are the problems that influence the risk of accidents. In most cases, the probability of major accidents is very low, but their consequences can be extreme, resulting in numerous fatalities and extensive environmental damage. An example is the Deepwater Horizon disaster in the Gulf of Mexico in 2010. Decision making under such circumstances (i.e., under high stake and complexity) is challenging, where expected utility calculations typically are not sufficient to provide a robust foundation. This article focuses on this specific category of risk-related decision problems, aiming to develop a decision analysis approach to enhance decision makers’ understanding on the features of their decision problems. This can in turn form the basis for tailoring the information needed for decision making. The proposed approach is a multi-dimensional approach, which includes seven dimensions: criticality, uniqueness, structuredness, complicatedness, dynamic, residual uncertainty, and problem trigger. Real-life examples are analyzed to illustrate and assess the proposed multi-dimensional approach. Overall, an improved understanding of risk-related decisions problem can be aided by the proposed approach, which indirectly helps to prevent accidents and maintain safety by predicting the human decision-making process, potential decision-making errors, and identifying decision support requirements.