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.

与风险相关的决策问题是影响事故风险的问题。在大多数情况下，发生重大事故的概率非常低，但其后果可能是极端的，导致大量人员死亡和广泛的环境破坏。一个例子是 2010 年墨西哥湾的深水地平线灾难。在这种情况下（即在高风险和复杂性的情况下）做出决策具有挑战性，预期效用计算通常不足以提供坚实的基础。本文重点关注这一特定类别的风险相关决策问题，旨在开发一种决策分析方法，以增强决策者对其决策问题特征的理解。这反过来又可以成为定制决策所需信息的基础。所提出的方法是一种多维方法，包括七个维度：关键性、独特性、结构化、复杂性、动态性、剩余不确定性和问题触发。通过分析现实生活中的例子来说明和评估所提出的多维方法。总体而言，所提出的方法可以帮助提高对风险相关决策问题的理解，通过预测人类决策过程、潜在决策错误和识别决策支持需求，间接有助于预防事故和维护安全。