Use and Misuse of MCDA to Support Decision Making Informed by Risk

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.