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Over the past decade, terrorism risk has become a prominent consideration in protecting the well‐being of individuals and organizations. More recently, there has been interest in not only quantifying terrorism risk, but also placing it in the context of an all‐hazards environment in which consideration is given to accidents and natural hazards, as well as intentional acts. This article discusses the development of a regional terrorism risk assessment model designed for this purpose. The approach taken is to model terrorism risk as a dependent variable, expressed in expected annual monetary terms, as a function of attributes of population concentration and critical infrastructure. This allows for an assessment of regional terrorism risk in and of itself, as well as in relation to man‐made accident and natural hazard risks, so that mitigation resources can be allocated in an effective manner. The adopted methodology incorporates elements of two terrorism risk modeling approaches (event‐based models and risk indicators), producing results that can be utilized at various jurisdictional levels. The validity, strengths, and limitations of the model are discussed in the context of a case study application within the United States.

This article presents a framework for economic consequence analysis of terrorism countermeasures. It specifies major categories of direct and indirect costs, benefits, spillover effects, and transfer payments that must be estimated in a comprehensive assessment. It develops a spreadsheet tool for data collection, storage, and refinement, as well as estimation of the various components of the necessary economic accounts. It also illustrates the usefulness of the framework in the first assessment of the tradeoffs between enhanced security and changes in commercial activity in an urban area, with explicit attention to the role of spillover effects. The article also contributes a practical user interface to the model for emergency managers.

Layered defenses are necessary for protecting the public from terrorist attacks. Designing a system of such defensive measures requires consideration of the interaction of these countermeasures. In this article, we present an analysis of a layered security system within the lower Manhattan area. It shows how portfolios of security measures can be evaluated through portfolio decision analysis. Consideration is given to the total benefits and costs of the system. Portfolio diagrams are created that help communicate alternatives among stakeholders who have differing views on the tradeoffs between security and economic activity.

On the "influence of scenarios to priorities" in risk and security programs / Heimir Thorisson, James H. Lambert -- Survey of risk analytic guidelines across the government / Isaac Maya, Lily Doyle, Amelia Liu, Francine Tran, Robert Creighton, Charles Woo -- An overview of risk modeling methods and approaches for national security / Samrat Chatterjee, Robert T. Brigantic, Angela M. Waterworth -- Comparative risk rankings in support of homeland security strategic plans / Russell Lundberg -- A data science workflow for discovering spatial patterns among terrorist attacks and infrastructure / Daniel Fortin, Thomas Johansen, Samrat Chatterjee, George Muller, Christine Noonan -- Effects of credibility of retaliation threats in deterring smuggling of nuclear weapons / Xiaojun Shan, Jun Zhuang -- Disutility of mass relocation after a severe nuclear accident / Vicki M. Bier, Shuji Liu -- Scheduling federal air marshals under uncertainty / Keith W. DeGregory, Rajesh Ganesan -- Decision theory for network security : active sensing for detection and prevention of data exfiltration / Sara M. McCarthy, Arunesh Sinha, Milind Tambe, Pratyusa Manadhatha -- Measurement of cyber resilience from an economic perspective / Adam Z. Rose, Noah Miller -- Responses to cyber near-misses : a scale to measure individual differences / Jinshu Cui, Heather Rosoff, Richard S. John -- An interactive web-based decision support system for mass dispensing, emergency preparedness, and biosurveillance / Eva K. Lee, Ferdinand H. Pietz, Chien-Hung Chen, Yifan Liu -- Critical infrastructure risk assessments : measuring critical infrastructure protection and resilience in an all-hazards environment / Julia Phillips, Frédéric Petit -- Risk analysis methods in resilience modeling : an overview of critical infrastructure applications / Hiba Baroud -- Optimal resource allocation model to prevent, prepare, and respond to multiple disruptions, with application to the Deepwater Horizon oil spill and Hurricane Katrina / Cameron A. MacKenzie, Amro Al-Kazimi -- Inoperability input-output modeling of electric power disruptions / Joost R. Santos, Sheree Ann Pagsuyoin, Christian Yip -- Quantitative assessment of transportation network vulnerability with dynamic traffic simulation methods / Venkateswaran Shekar, Lance Fiondella -- Infrastructure monitoring for health and security / Prodyot K. Basu -- Exploring metaheuristic approaches for solving the traveling salesman problem applied to emergency planning and response / Ramakrishna Tipireddy, Javier Rubio-Herrero, Samrat Chatterjee, Satish Chikkagoudar, George Muller.

AbstractArtificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI‐based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI‐based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI‐based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI‐based applications.

AbstractCritical infrastructures such as cyber‐physical energy systems (CPS‐E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS‐E is critical to ensure societal well‐being and economic prosperity. Automated control is key for real‐time operations and may be mathematically cast as a sequential decision‐making problem under uncertainty. Emergence of data‐driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision‐making problems for risk‐based robust CPS‐E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS‐E applications, addressing the effect of risk preferences across multiple RL methods, and development of open‐source domain‐aware simulation environments for RL experimentation within a CPS‐E context. This article systematically analyzes the applicability of four types of RL methods (model‐free, model‐based, hybrid model‐free and model‐based, and hierarchical) for risk‐based robust CPS‐E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk‐averse, risk‐neutral, and risk‐seeking) through the development and application of an open‐source simulation environment. Motivating numerical simulation examples include representative single‐zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.