Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

Understanding and Reducing Inconsistency in Seatbelt‐Use Decisions: Findings from a Cardinal Decision Issue Perspective

This article has two aims. The first is to present results that partly explain why some automobile drivers choose to use their seatbelts only part time, thereby exposing themselves to unnecessary risk. The second is to offer and illustrate the “cardinal decision issue perspective”⁽¹⁾ as a tool for guiding research and development efforts that focus on complex real‐life decision behaviors that can entail wide varieties of risk, including but not limited to inconsistent seatbelt use. Each of 24 young male participants drove an instrumented vehicle equipped to record continuously seatbelt use as well as other driving data. After all trips were finished, each participant completed an interview designed to reconstruct how he made randomly selected seatbelt‐use decisions under specified conditions. The interview also examined whether and how drivers established “decision policies” regarding seatbelt use. Such policies were good predictors of inconsistent seatbelt use. Drivers who had previously adopted policies calling for consistent seatbelt use were significantly more likely than others to actually drive belted. Meta‐decisions about seatbelt policy adoption appeared to rest on factors such as whether the driver had ever been asked to consider selecting a policy. Whether a driver made an ad hoc, on‐the‐spot seatbelt‐use decision was associated with a perceived need to make such a decision. Finally, participants with full‐time policies were especially likely to deploy their seatbelts by default, without recognizing the need to decide about belt use on a trip‐by‐trip basis. We end with recommendations for reducing inconsistencies in seatbelt use in actual practice.     

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.