Management Science & Information Systems Department, University of Massachusetts Boston, Boston, MA, USA.
Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA.
Risk Anal. 2021 Sep;41(9):1513-1521. doi: 10.1111/risa.13631. Epub 2020 Nov 10.
Recent guidelines for risk-informed decision making (RIDM) provide a gold-standard for how to incorporate probabilistic risk models in conjunction with other considerations in a decision process. Nevertheless, risk quantification using probabilistic and statistical methods is difficult in situations where threat, vulnerability, and consequences are highly uncertain and risk quantification. In such situations a wider variety of methods could be employed, which we call decision making informed by risk (DMIR) combining risk and decision analytics. Risk informed decision making (RIDM) can be considered as a special case of DMIR. Multi criteria decision analysis (MCDA) often serves as a basis for DMIR in order to flexibly accommodate different levels of analytical detail. DMIR often involves artful use of proxy variables that correlate with, and are more measurable than, underlying factors of interest. This article introduces the notion of DMIR and discusses the use of MCDA in its application in the context of risk-based problems. MCDA-based risk analyses identify metrics associated with threats of concern and system vulnerabilities, characterize the way in which alternative actions can affect these threats and vulnerabilities, and ultimately synthesize this information to compare, prioritize, or select alternative mitigation strategies. Simple linear additive MCDA models often integrate these inputs, but the same simplicity can limit such approaches and create pitfalls and more advanced models including multiplicative relationships can be warranted. This essay qualitatively explores the critical practitioner questions of how and when the use of linear multicriteria models creates significant problems, and how to avoid them.
近期风险知情决策 (RIDM) 指南为如何在决策过程中结合概率风险模型和其他考虑因素提供了黄金标准。然而，在威胁、脆弱性和后果高度不确定且风险量化困难的情况下，使用概率和统计方法进行风险量化是具有挑战性的。在这种情况下，可以采用更广泛的方法，我们称之为风险知情决策 (DMIR)，它将风险和决策分析结合起来。风险知情决策 (RIDM) 可以被视为 DMIR 的一个特例。多准则决策分析 (MCDA) 通常作为 DMIR 的基础，以灵活适应不同层次的分析细节。DMIR 通常涉及巧妙地使用与感兴趣的潜在因素相关且更可衡量的代理变量。本文介绍了 DMIR 的概念，并讨论了在基于风险的问题背景下，MCDA 在其应用中的使用。基于 MCDA 的风险分析确定与关注威胁和系统脆弱性相关的指标，描述替代行动如何影响这些威胁和脆弱性的方式，并最终综合这些信息来比较、优先排序或选择替代缓解策略。简单的线性加性 MCDA 模型通常会整合这些输入，但同样的简单性可能会限制这些方法并产生陷阱，更先进的模型包括乘法关系可能是有必要的。本文定性探讨了关于何时以及如何使用线性多准则模型会产生重大问题，以及如何避免这些问题的关键实践问题。
