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AbstractThe COVID‐19 pandemic has created a multitude of decision problems for a variety of fields. Questions from the seriousness and breadth of the problem to the effectiveness of proposed mitigation measures have been raised. We assert that the decision sciences have a crucial role to play here, as the questions requiring answers involve complex decision making under both uncertainty and ambiguity. The collection, processing, and analysis of data is critical in providing a useful response—especially as information of fundamental importance to such decision making (base rates and transmission rates) is lacking. We propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people, as laboratory diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients presenting with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. As these stances may be at odds with triage practices among medical practitioners, they highlight the important role the decision analyst can play in responding to the challenges of the COVID‐19 pandemic.

A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering‐in‐place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.

This article reports an extension of the Carnegie Mellon risk‐ranking method to incorporate ecological risks and their attributes. On the basis of earlier risk‐perception studies, we identified a set of 20 relevant attributes for describing health, safety, and environmental hazards in standardized risk summary sheets. In a series of three ranking sessions, 23 laypeople ranked 10 such hazards in a fictional Midwestern U.S. county using both holistic and multiattribute ranking procedures. Results were consistent with those from previous studies involving only health and safety hazards, providing additional evidence for the validity of the method and the replicability of the resulting rankings. Holistic and multiattribute risk rankings were reasonably consistent both for individuals and for groups. Participants reported that they were satisfied with the procedures and results, and indicated their support for using the method to advise real‐world risk‐management decisions. Agreement among participants increased over the course of the exercise, perhaps because the materials and deliberations helped participants to correct their misconceptions and clarify their values. Overall, health and safety attributes were judged more important than environmental attributes. However, the overlap between the importance rankings of these two sets of attributes suggests that some information about environmental impacts is important to participants' judgments in comparative risk‐assessment tasks.

A deliberative method for ranking risks was evaluated in a study involving 218 risk managers. Both holistic and multiattribute procedures were used to assess individual and group rankings of health and safety risks facing students at a fictitious middle school. Consistency between the rankings that emerged from these two procedures was reasonably high for individuals and for groups, suggesting that these procedures capture an underlying construct of riskiness. Participants reported high levels of satisfaction with their groups' decision‐making processes and the resulting rankings, and these reports were corroborated by regression analyses. Risk rankings were similar across individuals and groups, even though individuals and groups did not always agree on the relative importance of risk attributes. Lower consistency between the risk rankings from the holistic and multiattribute procedures and lower agreement among individuals and groups regarding these rankings were observed for a set of high‐variance risks. Nonetheless, the generally high levels of consistency, satisfaction, and agreement suggest that this deliberative method is capable of producing risk rankings that can serve as informative inputs to public risk‐management decision making.

Risk ranking offers a potentially powerful means for gathering public input to help set risk‐management priorities. In most rankings conducted to date, the categories and attributes used to describe the risks have varied widely, the materials and procedures have not been designed to facilitate comparisons among risks on all important attributes, and the validity and reproducibility of the resulting rankings have not been assessed. To address these needs, a risk‐ranking method was developed in which risk experts define and categorize the risks to be ranked, identify the relevant risk attributes, and characterize the risks in a set of standardized risk summary sheets, which are then used by lay or other groups in structured ranking exercises. To evaluate this method, a test bed involving 22 health and safety risks in a fictitious middle school was created. This article provides an overview of the risk‐ranking method and describes the challenges faced in designing the middle school test bed. A companion article in this issue reports on the validity of the ranking procedures and the level of agreement among risk managers regarding ranking of risks and attributes.