International Trade Inoperability Input-Output Model (IT-IIM): Theory and Application

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).     

Probabilistic Risk Analysis and Terrorism Risk

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.     

Strategic Preparedness for Recovery from Catastrophic Risks to Communities and Infrastructure Systems of Systems

Natural and human‐induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human–organizational–cyber–physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta‐modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision‐making process and its associated actions. These must be: implemented in advance of a natural or human‐induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers’ implicit and explicit acceptance of various risks and tradeoffs). The inoperability input‐output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies.     

Extreme Risk Analysis of Interdependent Economic and Infrastructure Sectors

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.     

Risk Framework for an Organizational System With Major Components

Many large organizations accomplish their various functions through interactions across their major components. Components refers to functional entities within a large complex organization, such as business sectors, academic departments, or regional divisions. The dependency between the various components can cause risk to propagate through their overall system. This article presents a risk assessment framework that integrates risk across a diverse set of components to the overall organization functions. This project addresses three major challenges: aggregating risk, estimating component interdependencies including cycles of dependencies, and propagating risk across components. The framework aggregates risk assessments through a value function for severity that is evaluated at the expected outcome of accomplishing planned goals in terms of performance, schedule, and resources. The value function, which represents risk tolerance, scales between defined points corresponding to failure and success. Different risk assessment may be aggregated together. This article presents a novel approach to establishing relationships between the various components. This article develops and compares three network risk propagation models that characterize the overall organizational risk. The U.S. Air Force has applied this risk framework to evaluate success in hypothetical future wars. The analysts employing this risk framework have informed billions of dollars of strategic investment decisions.     

Flood Risk Management: US Army Corps of Engineers and Layperson Perceptions

Recent severe storm experiences in the U.S. Gulf Coast illustrate the importance of an integrated approach to flood preparedness planning that harmonizes stakeholder and agency efforts. Risk management decisions that are informed by and address decision maker and stakeholder risk perceptions and behavior are essential for effective risk management policy. A literature review and two expert models/mental models studies were undertaken to identify areas of importance in the flood risk management process for layperson, non‐USACE‐expert, and two USACE‐expert groups. In characterizing and mapping stakeholder beliefs about risks in the literature onto current risk management practice, recommendations for accommodating and changing stakeholder perceptions of flood risks and their management are identified. Needs of the U.S. Army Corps of Engineers (USACE) flood preparedness and response program are discussed in the context of flood risk mental models.     

The Role of Behavioral Responses in the Total Economic Consequences of Terrorist Attacks on U.S. Air Travel Targets

U.S. airports and airliners are prime terrorist targets. Not only do the facilities and equipment represent high‐value assets, but the fear and dread that is spread by such attacks can have tremendous effects on the U.S. economy. This article presents the methodology, data, and estimates of the macroeconomic impacts stemming from behavioral responses to a simulated terrorist attack on a U.S. airport and on a domestic airliner. The analysis is based on risk‐perception surveys of these two scenarios. The responses relate to reduced demand for airline travel, shifts to other modes, spending on nontravel items, and savings of potential travel expenditures by U.S. resident passengers considering flying domestic routes. We translate these responses to individual spending categories and feed these direct impact results into a computable general equilibrium (CGE) model of the U.S. economy to ascertain the indirect and total impacts on both the airline industry and the economy as a whole. Overall, the estimated impacts on GDP of both types of attacks exceed $10B. We find that the behavioral economic impacts are almost an order of magnitude higher than the ordinary business interruption impacts for the airliner attack and nearly two orders of magnitude higher for the airport attack. The results are robust to sensitivity tests on the travel behavior of U.S. residents in response to terrorism.     

Assessing the Benefits and Costs of Homeland Security Research: A Risk-Informed Methodology with Applications for the U.S. Coast Guard

This article describes a methodology for risk-informed benefit–cost analyses of homeland security research products. The methodology is field-tested with 10 research products developed for the U.S. Coast Guard. Risk-informed benefit–cost analysis is a tool for risk management that integrates elements of risk analysis, decision analysis, and benefit–cost analysis. The cost analysis methodology includes a full-cost accounting of research projects, starting with initial fundamental research costs and extending to the costs of implementation of the research products and, where applicable, training, maintenance, and upgrade costs. The benefits analysis methodology is driven by changes in costs and risks leading to five alternative models: cost savings at the same level of security, increased security at the same cost, signal detection improvements, risk reduction by deterrence, and value of information. The U.S. Coast Guard staff selected 10 research projects to test and generalize the methodology. Examples include tools to improve the detection of explosives, reduce the costs of harbor patrols, and provide better predictions of hurricane wind speeds and floods. Benefits models and estimates varied by research project and many input parameters of the benefit estimates were highly uncertain, so risk analysis for sensitivity testing and simulation was important. Aggregating across the 10 research products, we found an overall median net present value of about $385 million, with a range from $54 million (5th percentile) to $877 million (95th percentile). Lessons learned are provided for future applications.     

Risk Modeling, Assessment, and Management of Lahar Flow Threat

The 1991 eruption of Mount Pinatubo in the Philippines is considered one of the most violent and destructive volcanic activities in the 20th century. Lahar is the Indonesian term for volcanic ash, and lahar flows resulting from the massive amount of volcanic materials deposited on the mountain's slope posed continued post-eruption threats to the surrounding areas, destroying lives, homes, agricultural products, and infrastructures. Risks of lahar flows were identified immediately after the eruption, with scientific data provided by the Philippine Institute of Volcanology, the U.S. Geological Survey, and other research institutions. However, competing political, economic, and social agendas subordinated the importance of scientific information to policy making. Using systemic risk analysis and management, this article addresses the issues of multiple objectives and the effective integration of scientific techniques into the decision-making process. It provides a modeling framework for identifying, prioritizing, and evaluating policies for managing risk. The major considerations are: (1) applying a holistic approach to risk analysis through hierarchical holographic modeling, (2) applying statistical methods to gain insight into the problem of uncertainty in risk assessment, (3) using multiobjective trade-off analysis to address the issue of multiple decisionmakers and stakeholders in the decision-making process, (4) using the conditional expected value of extreme events to complement and supplement the expected value in quantifying risk, and (5) assessing the impacts of multistage decisions. Numerical examples based on ex post data are formulated to illustrate applications to various problems. The resulting framework from this study can serve as a general baseline model for assessing and managing risks of natural disasters, which the Philippines' lead agency-the National Disaster Coordinating Council (NDCC)-and other related organizations can use for their decision-making processes.     

Probabilistic Risk Analysis for a High-Level Radioactive Waste Repository

A probabilistic risk analysis (PRA) for a high-level radioactive waste repository is very important since it gives an estimate of its health impacts, allowing comparisons to be made with the health impacts of competing technologies. However, it is extremely difficult to develop a credible PRA for a specific repository site because of large uncertainties in future climate, hydrology, geological processes, etc. At best, such a PRA would not be understandable to the public. An alternative proposed here is to develop a PRA for an average U.S. site, taking all properties of the site to be the U.S. average. The results are equivalent to the average results for numerous randomly selected sites. Such a PRA is presented here; it is easy to understand, and it is not susceptible to substantial uncertainty. Applying the results to a specific repository site then requires only a simple, intuitively acceptable "leap of faith" in assuming that with large expenditures of effort and money, experts can select a site that would be at least as secure as a randomly selected site.     

Ecological Risk Assessment Framework for Low-Altitude Aircraft Overflights: I. Planning the Analysis and Estimating Exposure

An ecological risk assessment framework for low-altitude aircraft overflights was developed, with special emphasis on military applications. The problem formulation and exposure analysis phases are presented in this article; an analysis of effects and risk characterization is presented in a companion article. The intent of this article is threefold: (1) to illustrate the development of a generic framework for the ecological risk assessment of an activity, (2) to show how the U.S. Environmental Protection Agency's ecological risk assessment paradigm can be applied to an activity other than the release of a chemical, and (3) to provide guidance for the assessment of ecological risks from low-altitude aircraft overflights. The key stressor for low-altitude aircraft overflights is usually sound, although visual and physical (collision) stressors may also be significant. Susceptible and regulated wildlife populations are the major assessment endpoint entities, although plant communities may be impacted by takeoffs and landings. The exposure analysis utilizes measurements of wildlife locations, measurements of sound levels at the wildlife locations, measurements of slant distances from aircraft to wildlife, models that extrapolate sound from the source aircraft to the ground, and bird-strike probability models. Some of the challenges to conducting a risk assessment for aircraft overflights include prioritizing potential stressors and endpoints, choosing exposure metrics that relate to wildlife responses, obtaining good estimates of sound or distance, and estimating wildlife locations.     

Characterizing Environmental Harm: Developments in an Approach to Strategic Risk Assessment and Risk Management

Environmental policymakers and regulators are often in the position of having to prioritize their actions across a diverse range of environmental pressures to secure environmental protection and improvements. Information on environmental issues to inform this type of strategic analysis can be disparate; it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy analysis and comparison. Nevertheless, judgments are required on the significance of various environmental pressures and on the inherent uncertainties to inform strategic assessments such as “state of the environment” reports. How can decisionmakers go about this type of strategic and comparative risk analysis? In an attempt to provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has conducted a program of developmental research on strategic risk assessment since 1996. The tools developed under this program use the concept of “environmental harm” as a common metric, viewed from technical, social, and economic perspectives, to analyze impacts from a range of environmental pressures. Critical to an informed debate on the relative importance of these perspectives is an understanding and analysis of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to actual or potential environmental harm from a range of hazards. Recent developments in our approach, described herein, allow a presentation of the analysis in a structured fashion so as to better inform risk‐management decisions.     

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.     

突发事件公众准备意愿的影响因素——基于实证数据的分析

突发事件是"低概率、高损失"的极端事件。突发事件风险客观存在,公众的应对准备意愿及行为对减缓突发事件风险及降低突发事件损失有着重要影响。但实际上,公众普遍表现出较弱的准备意愿,很少主动采取准备措施及行为。为了解释公众为避免突发事件损失而事先采取应对准备的行为差异,本文基于实证调查数据,将保护动机理论（PMT）应用于公众准备意愿的预测研究,建立突发事件公众认知与准备意愿理论模型,运用多元回归分析及结构方程模型方法,验证公众风险认知、应对认知与准备意愿之间的路径关系。其中风险认知包括可能性和严重性认知两个变量,应对认知包括应对效能、自我效能和应对成本认知三个变量。实证数据通过问卷调查的方式获取,调查对象涉及湖北省武汉市七大行政区域共405位城区居民。数据分析结果表明,公众的准备意愿受到风险认知及应对认知的共同影响,相比于风险认知,应对认知对准备意愿有更大的解释效力;年龄、教育程度、收入等三类人口统计学特征对准备意愿有一定的解释效力。研究结论显示,为了促使公众形成准备意愿,风险沟通不仅应考虑突发事件风险发生的可能性及后果的严重性,更需要关注应对效能、自我效能以及应对成本等对公众准备意愿有着显著影响的因素。     

The Impact of Mega‐Catastrophes on Insurers: An Exposure‐Based Analysis of the U.S. Homeowners’ Insurance Market

Insurance is a key risk‐sharing mechanism that protects citizens and governments from the losses caused by natural catastrophes. Given the increase in the frequency and intensity of natural catastrophes over recent years, this article analyzes the performance effects of mega‐catastrophes for U.S. insurance firms using a measure of market expectations. Specifically, we analyze the share price losses of insurance firms in response to catastrophe events to ascertain whether mega‐catastrophes significantly damage the performance of insurers and whether different types of mega‐catastrophes have different impacts. The main message from our analysis is that the impact of mega‐catastrophes on insurers has not been too damaging. While the exact impact of catastrophes depends on the nature of the event and the degree of competition within the relevant insurance market (less competition allows insurers to recoup catastrophe losses through adjustments to premiums), our overall results suggest that U.S. insurance firms can adequately manage the risks and costs of mega‐catastrophes. From a public policy perspective, our results show that insurance provides a robust means of sharing catastrophe losses to help reduce the financial consequences of a catastrophe event.     

Guiding Resource Allocations Based on Terrorism Risk

Establishing tolerable levels of risk is one of the most contentious and important risk management decisions. With every regulatory or funding decision for a risk management program, society decides whether or not risk is tolerable. The Urban Area Security Initiative (UASI) is a Department of Homeland Security (DHS) grant program designed to enhance security and overall preparedness to prevent, respond to, and recover from acts of terrorism by providing financial assistance for planning, equipment, training, and exercise needs of large urban areas. After briefly reviewing definitions of terrorism risk and rationales for risk-based resource allocation, this article compares estimates of terrorism risk in urban areas that received UASI funding in 2004 to other federal risk management decisions. This comparison suggests that UASI allocations are generally consistent with other federal risk management decisions. However, terrorism risk in several cities that received funding is below levels that are often tolerated in other risk management contexts. There are several reasons why the conclusions about terrorism risk being de minimis in specific cities should be challenged. Some of these surround the means used to estimate terrorism risk for this study. Others involve the comparison that is made to other risk management decisions. However, many of the observations reported are valid even if reported terrorism risk estimates are several orders of magnitude too low. Discussion of resource allocation should be extended to address risk tolerance and include explicit comparisons, like those presented here, to other risk management decisions.     

Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses

The Environmental Benefits Mapping and Analysis Program (BenMAP) is a software tool developed by the U.S. Environmental Protection Agency (EPA) that is widely used inside and outside of EPA to produce quantitative estimates of public health risks from fine particulate matter (PM_2.5). This article discusses the purpose and appropriate role of a risk analysis tool to support risk management deliberations, and evaluates the functions of BenMAP in this context. It highlights the importance in quantitative risk analyses of characterization of epistemic uncertainty, or outright lack of knowledge, about the true risk relationships being quantified. This article describes and quantitatively illustrates sensitivities of PM_2.5 risk estimates to several key forms of epistemic uncertainty that pervade those calculations: the risk coefficient, shape of the risk function, and the relative toxicity of individual PM_2.5 constituents. It also summarizes findings from a review of U.S.‐based epidemiological evidence regarding the PM_2.5 risk coefficient for mortality from long‐term exposure. That review shows that the set of risk coefficients embedded in BenMAP substantially understates the range in the literature. We conclude that BenMAP would more usefully fulfill its role as a risk analysis support tool if its functions were extended to better enable and prompt its users to characterize the epistemic uncertainties in their risk calculations. This requires expanded automatic sensitivity analysis functions and more recognition of the full range of uncertainty in risk coefficients.     

Managing the impacts of large public programs

This paper is the result of a research project which investigated the impact of the U.S. space program on the American economy. Based on their field studies of past space program impacts, the authors develop several conclusions regarding the overall management and strategic planning of large public programs. In this article, the author outlines a structured approach to strategic planning and evaluation which will aid public program managers to respond to the challenge of impact management.     

Exploring the Predictors of Organizational Preparedness for Natural Disasters

There is an extensive body of research on the determinants of disaster preparedness at the individual and household levels. The same cannot be said for the organizational level. Hence, the purpose of this study is to shed light on the predictors of organizational preparedness for natural disasters. Since leaders of organizations have an incentive to overstate their level of preparedness and because surveys of organizational leaders suffer from selection bias and low response rates, we take the novel approach of interviewing employees about the organizations that employ them. Using an online survey, we collected information from a national sample of 2,008 U.S. employees and estimated the predictors of preparedness at the organizational level. We find, among other results, that organization size (facility level) is a consistent predictor of preparedness at the organizational level. We conclude with policy recommendations and outline an agenda for future research on organizational preparedness for natural disasters.     

Integrated Analysis: Combining Risk and Economic Assessments While Preserving the Separation of Powers

This article presents a process for an integrated policy analysis that combines risk assessment and benefit-cost analysis. This concept, which explicitly combines the two types of related analyses, seems to contradict the long-accepted risk analysis paradigm of separating risk assessment and risk management since benefit-cost analysis is generally considered to be a part of risk management. Yet that separation has become a problem because benefit-cost analysis uses risk assessment results as a starting point and considerable debate over the last several years focused on the incompatibility of the use of upper bounds or "safe" point estimates in many risk assessments with benefit-cost analysis. The problem with these risk assessments is that they ignore probabilistic information. As advanced probabilistic techniques for risk assessment emerge and economic analysts receive distributions of risks instead of point estimates, the artificial separation between risk analysts and the economic/decision analysts complicates the overall analysis. In addition, recent developments in countervailing risk theory suggest that combining the risk and benefit-cost analyses is required to fully understand the complexity of choices and tradeoffs faced by the decisionmaker. This article also argues that the separation of analysis and management is important, but that benefit-cost analysis has been wrongly classified into the risk management category and that the analytical effort associated with understanding the economic impacts of risk reduction actions need to be part of a broader risk assessment process.