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Risk Analysis in Engineering and Economics is required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examin

The United Nations Office for Disaster Risk Reduction reported that the 2011 natural disasters, including the earthquake and tsunami that struck Japan, resulted in $366 billion in direct damages and 29,782 fatalities worldwide. Storms and floods accounted for up to 70% of the 302 natural disasters worldwide in 2011, with earthquakes producing the greatest number of fatalities. Average annual losses in the United States amount to about $55 billion. Enhancing community and system resilience could lead to massive savings through risk reduction and expeditious recovery. The rational management of such reduction and recovery is facilitated by an appropriate definition of resilience and associated metrics. In this article, a resilience definition is provided that meets a set of requirements with clear relationships to the metrics of the relevant abstract notions of reliability and risk. Those metrics also meet logically consistent requirements drawn from measure theory, and provide a sound basis for the development of effective decision‐making tools for multihazard environments. Improving the resiliency of a system to meet target levels requires the examination of system enhancement alternatives in economic terms, within a decision‐making framework. Relevant decision analysis methods would typically require the examination of resilience based on its valuation by society at large. The article provides methods for valuation and benefit‐cost analysis based on concepts from risk analysis and management.

AbstractThe rise in economic disparity presents significant risks to global social order and the resilience of local communities. However, existing measurement science for economic disparity (e.g., the Gini coefficient) does not explicitly consider a probability distribution with information, deficiencies, and uncertainties associated with the underlying income distribution. This article introduces the quantification of Shannon entropy for income inequality across scales, including national‐, subnational‐, and city‐level data. The probabilistic principles of Shannon entropy provide a new interpretation for uncertainty and risk related to economic disparity. Entropy and information‐based conflict rise as world incomes converge. High‐entropy instances can resemble both happy and prosperous societies as well as a socialist–communist social structure. Low entropy signals high‐risk tipping points for anomaly and conflict detection with higher confidence. Finally, spatial–temporal entropy maps for U.S. cities offer a city risk profiling framework. The results show polarization of household incomes within and across Baltimore, Washington, DC, and San Francisco. Entropy produces reliable results at significantly reduced computational costs than Gini coefficients.

Sponsored by the Council on Disaster Risk Management Sea Level Rise and Coastal Infrastructure: Prediction, Risks, and Solutions analyzes the challenges posed by rising sea levels and climate change. Scientists estimate that global sea levels could rise by as much as 20 feet in this century, directly affecting about 100 million people worldwide. Although the problems stemming from higher sea levels are formidable, immediate actions can be identified and executed to lessen the impact of rising waters on coastal infrastructure and communities. Using a risk analysis and management framework, each chapter in this volume focuses on a facet of sea level rise, examining its associated risks and assessing its socioeconomic impact. From this information, appropriate long-term measures and mitigation strategies can be developed. Chapters consider such questions as: How can we model the impact of rising sea levels and increasingly intense tropical storms on coastal infrastructure? What strategies can be phased in to improve new construction? How can existing infrastructure best be targeted for retrofitting? How can risk models be designed to accommodate regional socioeconomic considerations? Engineers, scientists, and policymakers concerned with planning, design, and construction of coastal infrastructure will find this compact assessment useful, relevant, and thought-provoking.

A system that includes a number of terrorist cells is considered. The cells can consist of one or more terrorists. The current number of terrorist cells is further denoted by N(t), where t is a current time counted from any appropriate origin. The objective is to find the evolution of the system in terms of N(t) and some interpretable parameters, such as the initial number of the terrorist cells N0=N(0), the cell disabling rate constant λ (or the cell half‐life t1/2), and the rate of formation of new cells P. The cost‐effectiveness analysis, performed in the framework of the model, reveals that the effectiveness of disabling a terrorist cell is getting worse after 2–3 half‐lives of a cell, which shows that if the anti‐terrorist actions have not reached their goal during that time, the respective policy should be considered for revision, using the risk assessment consideration. Another important issue raised concerns balancing the efforts related to counterterrorism actions inside the system and the efforts protecting its borders. The respective data analysis is suggested and illustrated using simulated data.

The city of Washington, District of Columbia (DC) will face flooding, and eventual geographic changes, in both the short‐ and long‐term future because of sea level rise (SLR) brought on by climate change, including global warming. To fully assess the potential damage, a linear model was developed to predict SLR in Washington, DC, and its results compared to other nonlinear model results. Using geographic information systems (GIS) and graphical visualization, analytical models were created for the city and its underlying infrastructure. Values of SLR used in the assessments were 0.1 m for the year 2043 and 0.4 m for the year 2150 to model short‐term SLR; 1.0 m, 2.5 m, and 5.0 m were used for long‐term SLR. All necessary data layers were obtained from free data banks from the U.S. Geological Survey and Washington, DC government websites. Using GIS software, inventories of the possibly affected infrastructure were made at different SLR. Results of the analysis show that low SLR would lead to a minimal loss of city area. Damages to the local properties, however, are estimated at an assessment value of at least US$2 billion based on only the direct losses of properties listed in real estate databases, without accounting for infrastructure damages that include military installations, residential areas, governmental property, and cultural institutions. The projected value of lost property is in excess of US$24.6 billion at 5.0 m SLR.

This article proposes a quantitative risk assessment and management framework that supports strategic asset‐level resource allocation decision making for critical infrastructure and key resource protection. The proposed framework consists of five phases: scenario identification, consequence and criticality assessment, security vulnerability assessment, threat likelihood assessment, and benefit‐cost analysis. Key innovations in this methodology include its initial focus on fundamental asset characteristics to generate an exhaustive set of plausible threat scenarios based on a target susceptibility matrix (which we refer to as asset‐driven analysis) and an approach to threat likelihood assessment that captures adversary tendencies to shift their preferences in response to security investments based on the expected utilities of alternative attack profiles assessed from the adversary perspective. A notional example is provided to demonstrate an application of the proposed framework. Extensions of this model to support strategic portfolio‐level analysis and tactical risk analysis are suggested.

This article develops a quantitative all‐hazards framework for critical asset and portfolio risk analysis (CAPRA) that considers both natural and human‐caused hazards. Following a discussion on the nature of security threats, the need for actionable risk assessments, and the distinction between asset and portfolio‐level analysis, a general formula for all‐hazards risk analysis is obtained that resembles the traditional model based on the notional product of consequence, vulnerability, and threat, though with clear meanings assigned to each parameter. Furthermore, a simple portfolio consequence model is presented that yields first‐order estimates of interdependency effects following a successful attack on an asset. Moreover, depending on the needs of the decisions being made and available analytical resources, values for the parameters in this model can be obtained at a high level or through detailed systems analysis. Several illustrative examples of the CAPRA methodology are provided.

AbstractThe observed global sea level rise owing to climate change, coupled with the potential increase in extreme storms, requires a reexamination of existing infrastructural planning, construction, and management practices. Storm surge shows the effects of rising sea levels. The recent super storms that hit the United States (e.g., Hurricane Katrina in 2005, Sandy in 2012, Harvey and Maria in 2017) and China (e.g., Typhoon Haiyan in 2010) inflicted serious loss of life and property. Water level rise (WLR) of local coastal areas is a combination of sea level rise, storm surge, precipitation, and local land subsidence. Quantitative assessments of the impact of WLR include scenario identification, consequence assessment, vulnerability and flooding assessment, and risk management using inventory of assets from coastal areas, particularly population centers, to manage flooding risk and to enhance infrastructure resilience of coastal cities. This article discusses the impact of WLR on urban infrastructures with case studies of Washington, DC, and Shanghai. Based on the flooding risk analysis under possible scenarios, the property loss for Washington, DC, was evaluated, and the impact on the metro system of Shanghai was examined.