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AbstractMany factors affect the extent to which forecasts inform emergency responses. In a survey based on the Theory of Planned Behavior (TPB), 207 U.S. emergency managers (EMs) were asked about 1) their past and intended future use of short-term weather forecasts and recorded weather data, 2) the perceived limitations and 3a) their attitude toward the usefulness of such weather information, 3b) their attitude toward their job and toward uncertainty, 4) perceived social norms, and 5) self-assessed numeracy. Work experience was found to be the best predictor of whether an emergency manager relied on recorded weather data and short-term weather forecasts in the past or intends to do so in the future. Among TPB variables, mainly social expectations and data attitude drive the reliance on recorded weather data and short-term forecasts. The EMs' perception of the weather information's limitations is related to their perceptions of what their social surroundings think. In sum, this article sheds light on when and why EMs use weather data and forecasts and how training can be improved.

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.

We offer a general approach to predicting public compliance with emergency recommendations. It begins with a formal risk assessment of an anticipated emergency, whose parameters include factors potentially affecting and affected by behavior, as identified by social science research. Standard procedures are used to elicit scientific experts' judgments regarding these behaviors and dependencies, in the context of an emergency scenario. Their judgments are used to refine the model and scenario, enabling local emergency coordinators to predict the behavior of citizens in their area. The approach is illustrated with a case study involving a radiological dispersion device (RDD) exploded in downtown Pittsburgh, PA. Both groups of experts (national and local) predicted approximately 80–90% compliance with an order to evacuate workplaces and 60–70% compliance with an order to shelter in place at home. They predicted 10% lower compliance for people asked to shelter at the office or to evacuate their homes. They predicted 10% lower compliance should the media be skeptical, rather than supportive. They also identified preparatory policies that could improve public compliance by 20–30%. We consider the implications of these results for improving emergency risk assessment models and for anticipating and improving preparedness for disasters, using Hurricane Katrina as a further case in point.

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.

Decision analysts are frequently called on to help inform decision makers in cases involving considerable uncertainty. In such situations, expert elicitation of parameter values is frequently used to supplement more conventional research. Expert elicitations typically rely on small panels of experts. However, in cases where the information needed for risk management must draw on a broad range of disciplines or types of professional backgrounds and experience, a larger, more heterogeneous expert panel is needed. In this paper we develop a formal protocol and a suite of uncertainty measures for this work. The protocol uses formal survey methods to take advantage of variation in individual expert uncertainty and heterogeneity among experts as a means of quantifying and comparing sources of uncertainty about parameters of interest. We illustrate the use of this protocol with an expert elicitation on the distribution of foodborne illness in the United States across foods. In the survey, experts are asked to attribute illnesses associated with one of eleven major foodborne pathogens to the consumption of one of eleven categories of food. Results show how the distributions of multiple measures of uncertainty (e.g., agreement of experts and uncertainty in knowledge), made feasible by use of a large panel of experts, can help identify which of several types of risk management actions may be most appropriate.

Communication about risks offers a voluntary approach to reducing exposure to pollutants. Its adequacy depends on its impact on behavior. Estimating those impacts first requires characterizing current activities and their associated risk levels, and then predicting the effectiveness of risk‐reduction strategies. Characterizing the risks from chemical consumer products requires knowledge of both the physical and the behavioral processes that influence exposures. This article presents an integrated approach that combines consumer interviews, users' beliefs and behaviors, and quantitative exposure modeling. This model was demonstrated in the context of consumer exposure to a methylene chloride‐based paint stripper, showing how it could be used to evaluate current levels of risk and predict the effectiveness of proposed voluntary risk‐reduction strategies.

Any practical process of risk ranking must group hazards into a manageable number of categories. Defining such categories requires value choices that can have important implications for the rankings that result. Most risk‐management organizations will find it useful to begin defining categories in terms of environmental loadings or initiating events. However, the resulting categories typically need to be modified in light of other considerations. Risk‐ranking projects can benefit from considering several alternative categorization strategies and drawing upon elements of each in developing their final categorization of risks. In principle, conducting multiple ranking exercises by using different categorizations could be interesting and useful. In practice, agencies are unlikely to have either the resources or patience to do this, but other groups in society might. Done well, such additional independent rankings could add valuable inputs to democratic risk‐management decision making.