Can Overconfidence be Debiased by Low‐Probability/High‐Consequence Events?

During the first half of 2008, China suffered three natural disasters: a heavy snow storm, an outbreak of hand‐foot‐mouth disease, and a severe earthquake. The aim of the present study is to explore how low‐probability/high‐consequence events influence overconfidence. In Study 1, opportunity samples were obtained by recruiting residents in three different types of disaster‐hit areas to answer a peer‐comparison probability judgment questionnaire about 1 month after the corresponding disaster occurred. The performance of 539 participants in disaster‐hit areas was compared with that of 142 residents in a nondisaster area. The findings indicate that residents in disaster‐hit areas were less overconfident than those in the nondisaster area on both positive and negative events. In Study 2, we surveyed a total of 336 quake‐victims 4 and 11 months after the earthquake to examine whether the impact of disasters on overconfidence would decay with time. The resulting data indicate that the disaster victims became more overconfident as time elapsed. The overall findings suggest that low‐probability/high‐consequence events could make people less overconfident and more rational and seem to serve as a function of debiasing.     

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.     

Disasters as Learning Experiences or Disasters as Policy Opportunities? Examining Flood Insurance Purchases after Hurricanes

Flood insurance is a critical risk management strategy, contributing to greater resilience of individuals and communities. The occurrence of disasters has been observed to alter risk management choices, including the decision to insure. This has previously been explained by learning and behavioral biases. When it comes to flood insurance, however, federal disaster aid policy could also play a role since recipients of aid are required to maintain insurance. Using a database of flood insurance policies for all states on the Atlantic and Gulf coasts of the United States between 2001 and 2010, this article uses fixed effects models to examine how take‐up rates respond to the occurrence of hurricanes and tropical storms, as well as disaster declarations and aid requirements. Being hit by at least one hurricane in the previous year increases net flood insurance purchases by 7.2%. This effect dies out by three years after the storm. A presidential disaster declaration for floods increases take‐up rates by 6.7%. When disaster aid grants are made available to households, take‐up rates increase by 5%; this accounts for the majority of the increase in policies after occurrence of a hurricane. When the models are estimated taking into account which policies are required by disaster aid, hurricanes are estimated to lead to only a 1.5% increase in voluntary purchases. This overlooked federal policy that disaster aid recipients insure is responsible for a majority of insurance purchases postdisaster.     

Low‐Probability Flood Risk Modeling for New York City

The devastating impact by Hurricane Sandy (2012) again showed New York City (NYC) is one of the most vulnerable cities to coastal flooding around the globe. The low‐lying areas in NYC can be flooded by nor'easter storms and North Atlantic hurricanes. The few studies that have estimated potential flood damage for NYC base their damage estimates on only a single, or a few, possible flood events. The objective of this study is to assess the full distribution of hurricane flood risk in NYC. This is done by calculating potential flood damage with a flood damage model that uses many possible storms and surge heights as input. These storms are representative for the low‐probability/high‐impact flood hazard faced by the city. Exceedance probability‐loss curves are constructed under different assumptions about the severity of flood damage. The estimated flood damage to buildings for NYC is between US$59 and 129 millions/year. The damage caused by a 1/100‐year storm surge is within a range of US$2 bn–5 bn, while this is between US$5 bn and 11 bn for a 1/500‐year storm surge. An analysis of flood risk in each of the five boroughs of NYC finds that Brooklyn and Queens are the most vulnerable to flooding. This study examines several uncertainties in the various steps of the risk analysis, which resulted in variations in flood damage estimations. These uncertainties include: the interpolation of flood depths; the use of different flood damage curves; and the influence of the spectra of characteristics of the simulated hurricanes.     

Empirical Estimation of the Conditional Probability of Natech Events Within the United States

Natural disasters are the cause of a sizeable number of hazmat releases, referred to as “natechs.” An enhanced understanding of natech probability, allowing for predictions of natech occurrence, is an important step in determining how industry and government should mitigate natech risk. This study quantifies the conditional probabilities of natechs at TRI/RMP and SICS 1311 facilities given the occurrence of hurricanes, earthquakes, tornadoes, and floods. During hurricanes, a higher probability of releases was observed due to storm surge (7.3 releases per 100 TRI/RMP facilities exposed vs. 6.2 for SIC 1311) compared to category 1–2 hurricane winds (5.6 TRI, 2.6 SIC 1311). Logistic regression confirms the statistical significance of the greater propensity for releases at RMP/TRI facilities, and during some hurricanes, when controlling for hazard zone. The probability of natechs at TRI/RMP facilities during earthquakes increased from 0.1 releases per 100 facilities at MMI V to 21.4 at MMI IX. The probability of a natech at TRI/RMP facilities within 25 miles of a tornado was small (～0.025 per 100 facilities), reflecting the limited area directly affected by tornadoes. Areas inundated during flood events had a probability of 1.1 releases per 100 facilities but demonstrated widely varying natech occurrence during individual events, indicating that factors not quantified in this study such as flood depth and speed are important for predicting flood natechs. These results can inform natech risk analysis, aid government agencies responsible for planning response and remediation after natural disasters, and should be useful in raising awareness of natech risk within industry.     

基于Bayes推理的灾害演化GERT网络模型研究

灾害、衍生和次生灾害及其相互耦合使得灾情恶化,作为灾害对抗力量的抢险、避险、控制的救险等措施使得灾害向灾情减轻的方向转化,本文对灾害的这一动态演化过程进行描述,建立一种综合考虑灾害的自然演化与抢险救灾行动的基于Bayes推理的灾害演化GERT(Graph Evaluation and Review Technique)网络模型;把GERT网络方法和贝叶斯推理工具相结合,根据获得的新信息,对GERT网络中活动参数进行动态修正,对灾害的演化路径,各种主要状态的演化概率和时间进行动态预测、预警与评价;对衍生与次生灾害、抢险救灾行动等外界行为对系统演化的影响进行定性与定量分析,并给出定量评价结论。本文提供了灾害演化定性与定量结合的分析框架与工具,揭示灾害演化机理,为灾害发展态势的预测、预警与评价提供了一种新的研究方法和研究思路。     

Risk Perception and Disaster Preparedness in Immigrants and Canadian‐Born Adults: Analysis of a National Survey on Similarities and Differences

Research has documented that immigrants tend to experience more negative consequences from natural disasters compared to native‐born individuals, although research on how immigrants perceive and respond to natural disaster risks is sparse. We investigated how risk perception and disaster preparedness for natural disasters in immigrants compared to Canadian‐born individuals as justifications for culturally‐adapted risk communication and management. To this end, we analyzed the ratings on natural disaster risk perception beliefs and preparedness behaviors from a nationally representative survey (N = 1,089). Factor analyses revealed three underlying psychological dimensions of risk perception: external responsibility for disaster management, self‐preparedness responsibility, and illusiveness of preparedness. Although immigrants and Canadian‐born individuals shared the three‐factor structure, there were differences in the salience of five risk perception beliefs. Despite these differences, immigrants and Canadian‐born individuals were similar in the level of risk perception dimensions and disaster preparedness. Regression analyses revealed self‐preparedness responsibility and external responsibility for disaster management positively predicted disaster preparedness whereas illusiveness of preparedness negatively predicted disaster preparedness in both groups. Our results showed that immigrants’ risk perception and disaster preparedness were comparable to their Canadian‐born counterparts. That is, immigrant status did not necessarily yield differences in risk perception and disaster preparedness. These social groups may benefit from a risk communication and management strategy that addresses these risk perception dimensions to increase disaster preparedness. Given the diversity of the immigrant population, the model remains to be tested by further population segmentation.     

自然异动下关键基础设施网络风险研究

关键基础设施保护是突发事件应急管理的重要对象构成,确定风险和保护排序是其保护机制的基本依据。在自然异动约束下,本文将信息扩散理论的超越概率引入风险分析模型,研究跨区域网络级联失效造成的风险,提出了确定关键基础设施网络构件的风险等级及保护排序的方法。以覆冰灾害下电网为例,研究了电网中不同支路在覆冰灾害下面临风险程度,与基本脆弱点进行对比,验证以异动风险作为保护参考的合理性。     

Index for Predicting Insurance Claims from Wind Storms with an Application in France

For insurance companies, wind storms represent a main source of volatility, leading to potentially huge aggregated claim amounts. In this article, we compare different constructions of a storm index allowing us to assess the economic impact of storms on an insurance portfolio by exploiting information from historical wind speed data. Contrary to historical insurance portfolio data, meteorological variables show fewer nonstationarities between years and are easily available with long observation records; hence, they represent a valuable source of additional information for insurers if the relation between observations of claims and wind speeds can be revealed. Since standard correlation measures between raw wind speeds and insurance claims are weak, a storm index focusing on high wind speeds can afford better information. A storm index approach has been applied to yearly aggregated claim amounts in Germany with promising results. Using historical meteorological and insurance data, we assess the consistency of the proposed index constructions with respect to various parameters and weights. Moreover, we are able to place the major insurance events since 1998 on a broader horizon beyond 40 years. Our approach provides a meteorological justification for calculating the return periods of extreme‐storm‐related insurance events whose magnitude has rarely been reached.     

Societal Response to Hurricane Mitch and Intra‐ versus Intergenerational Equity Issues: Whose Norms Should Apply?

Late in the 1998 hurricane season, Central America was slammed by a devastating hurricane. Honduras, Nicaragua, El Salvador, Guatemala, and Belize were greatly impacted by Hurricane Mitch, one of the deadliest storms to affect the region in the past 200 years. The economies of each of these countries were badly affected. In the case of Honduras — at the time the fourth‐poorest country in Latin America — its president suggested that 50 years of progress had been wiped out by the floods and mudslides associated with this relatively short‐lived storm system. Humanitarian assistance poured into the region in the first months following the disaster. As of mid‐2000, various national, bilateral, international, and nongovernmental programs were in progress or on the drawing board for recovery, reconstruction, and renewed development of the worst affected countries. Using Honduras as a case study, some of the ethical issues that abound in the decisions of whom to help, when, and how to help them in the wake of such an extreme climate‐related human tragedy are examined. Should development assistance be focused on those who have been directly and adversely affected by this storm, or should the emphasis be on reducing the risk of exposure by future generations to such disasters? Is there yet another approach that seeks to protect future generations from similar harm while at the same time assisting present‐day victims to get through their hardships?     

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.     

On the Size Distribution of Macroeconomic Disasters

The coefficient of relative risk aversion is a key parameter for analyses of behavior toward risk, but good estimates of this parameter do not exist. A promising place for reliable estimation is rare macroeconomic disasters, which have a major influence on the equity premium. The premium depends on the probability and size distribution of disasters, gauged by proportionate declines in per capita consumption or gross domestic product. Long‐term national‐accounts data for 36 countries provide a large sample of disasters of magnitude 10% or more. A power‐law density provides a good fit to the size distribution, and the upper‐tail exponent, α, is estimated to be around 4. A higher α signifies a thinner tail and, therefore, a lower equity premium, whereas a higher coefficient of relative risk aversion, γ, implies a higher premium. The premium is finite if α > γ. The observed premium of 5% generates an estimated γ close to 3, with a 95% confidence interval of 2 to 4. The results are robust to uncertainty about the values of the disaster probability and the equity premium, and can accommodate seemingly paradoxical situations in which the equity premium may appear to be infinite.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

A Framework to Understand Extreme Space Weather Event Probability

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well‐being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst ) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments.     

Cross‐Cultural Differences in Risk Perceptions of Disasters

Public risk perceptions of mass disasters carry considerable influences, both psychologically and economically, despite their oft‐times imprecise nature. Prior research has identified the presence of an optimistic bias that affects risk perception, but there is a dearth of literature examining how these perceptions differ among cultures—particularly with regard to mass disasters. The present study explores differences among Japanese, Argentinean, and North American mental health workers in their rates of the optimistic bias in risk perceptions as contrasted between natural disasters and terrorist events. The results indicate a significant difference among cultures in levels of perceived risk that do not correspond to actual exposure rates. Japanese groups had the highest risk perceptions for both types of hazards and North Americans and Argentineans had the lowest risk perceptions for terrorism. Additionally, participants across all cultures rated risk to self as lower than risk to others (optimistic bias) across all disaster types. These findings suggest that cultural factors may have a greater influence on risk perception than social exposure, and that the belief that one is more immune to disasters compared to others may be a cross‐cultural phenomenon.     

洪水灾害风险分析的系统理论

从系统论的观点出发 ,提出了洪水灾害复杂大系统的概念 ,并以这一概念为基础 ,探讨了洪水灾害风险特征及洪水灾害风险评价的基本内容 ,提出并系统地阐述了以洪水危险性分析、承灾体易损性分析和洪水灾害灾情评估为核心内容的洪水灾害风险分析的系统理论     

State of the Art in Risk Analysis of Workforce Criticality Influencing Disaster Preparedness for Interdependent Systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.     

考虑供应风险的多源应急物资采购双层规划模型

自然灾害频繁发生,事故灾难不断涌现,公共卫生事件猝不及防,社会安全风险交织叠加,给人们的生产和生活带来了巨大的风险和不确定性。在突发事件背景下,考虑应急物资供应中的各类风险,激励多源应急物资的稳定供给,受到党和政府高度重视。应急物资供应有哪些风险,如何规避这些风险,针对多种渠道的采购,如何优化采购结构。这些问题是本论文关注的重点。本文以政府、制造型企业、受灾地区民众为决策主体的三级供应链为基础,构建了上层以整个供应链系统收益最大化为目标函数、下层以供应风险最小化为目标函数的双层规划模型,对不同情景下物资采购订单的分配进行研究。在此基础上,本文以某市洪涝灾害为例进行分析,证明了模型的可行性,并探究了供应风险变化对物资采购订单分配、以及整个供应链系统收益的影响,以期为政府应急物资采购决策提供参考性建议。     

Causes of Disaster: Sloppy Management

The loss of control associated with accidents and disasters can have severe negative consequences for the organizations involved. Some disasters are caused by inadequacies of management, or by unprofessional behaviour but others arise when disaster preconditions are generated as a result of the normal functioning of larger managerial and technical systems. During the incubation period preceding a major incident, therefore, two levels of correction need to be considered to avoid disasters. ‘Sloppy management’ of various kinds can be tackled by establishing, strengthening and asserting appropriate forms of management control. The system properties which constitute disaster preconditions are less immediately accessible to management control. At both levels managers in pursuit of high reliability should expect to have to make a range of trade-offs.     

Improved Methods for Estimating Flood Depth Exceedances Within Storm Surge Protection Systems

Contemporary studies conducted by the U.S. Army Corps of Engineers estimate probability distributions of flooding on the interior of ring levee systems by estimating surge exceedances at points along levee system boundaries, calculating overtopping volumes generated by this surface, then passing the resulting volumes of water through a drainage model to calculate interior flood depths. This approach may not accurately represent the exceedance probability of flood depths within the system interior; a storm producing 100‐year surge at one point is unlikely to simultaneously produce 100‐year surge levels everywhere around the system exterior. A conceptually preferred approach estimates surge and waves associated with a large set of storms. Each storm is run through the interior model separately, and the resulting flood depths are weighted by a parameterized likelihood of each synthetic storm. This results in an empirical distribution of flood depths accounting for geospatial variation in any individual storm's characteristics. This method can also better account for the probability of levee breaches or other system failures. The two methods can produce different estimates of flood depth exceedances and damage when applied to storm surge flooding in coastal Louisiana. Even differences in flood depth exceedances of less than 0.2 m can still produce large differences in projected damage. This article identifies and discusses differences in estimated flood depths and damage produced by each method within multiple Louisiana protection systems. The novel coupled dynamics approach represents a step toward enabling risk‐based design standards.