Decision analysis and risk models for land development affecting infrastructure systems

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development.     

Decision‐Making Analytics Using Plural Resilience Parameters for Adaptive Management of Complex Systems

It is critical for complex systems to effectively recover, adapt, and reorganize after system disruptions. Common approaches for evaluating system resilience typically study single measures of performance at one time, such as with a single resilience curve. However, multiple measures of performance are needed for complex systems that involve many components, functions, and noncommensurate valuations of performance. Hence, this article presents a framework for: (1) modeling resilience for complex systems with competing measures of performance, and (2) modeling decision making for investing in these systems using multiple stakeholder perspectives and multicriteria decision analysis. This resilience framework, which is described and demonstrated in this article via a real‐world case study, will be of interest to managers of complex systems, such as supply chains and large‐scale infrastructure networks.     

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial 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.     

Workforce/Population,Economy, Infrastructure,Geography, Hierarchy,and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.     

Supply Chain Vulnerability Analysis Using Scenario‐Based Input‐Output Modeling: Application to Port Operations

Disruptive events such as natural disasters, loss or reduction of resources, work stoppages, and emergent conditions have potential to propagate economic losses across trade networks. In particular, disruptions to the operation of container port activity can be detrimental for international trade and commerce. Risk assessment should anticipate the impact of port operation disruptions with consideration of how priorities change due to uncertain scenarios and guide investments that are effective and feasible for implementation. Priorities for protective measures and continuity of operations planning must consider the economic impact of such disruptions across a variety of scenarios. This article introduces new performance metrics to characterize resiliency in interdependency modeling and also integrates scenario‐based methods to measure economic sensitivity to sudden‐onset disruptions. The methods will be demonstrated on a U.S. port responsible for handling $36.1 billion of cargo annually. The methods will be useful to port management, private industry supply chain planning, and transportation infrastructure management.     

Risk analysis under attack: How risk science can address the legal,social, and reputational liabilities faced by risk analysts

The role of the risk analyst is critical in understanding and managing uncertainty. However, there is another type of uncertainty that is rarely discussed: The legal, social, and reputational liabilities of the risk analyst. Recent events have shown that professionals participating in risk analysis can be held personally liable. It is timely and important to ask: How can risk science guide risk analysis with consideration of those liabilities, particularly in response to emerging and unprecedented risk. This paper studies this topic by: (1) Categorizing how professionals with risk analysis responsibilities have historically been held liable, and (2) developing a framework to address uncertainty related to those potential liabilities. The result of this framework will enable individual analysts and organizations to investigate and manage the expectations of risk analysts and others as they apply risk principles and methods. This paper will be of interest to risk researchers, risk professionals, and industry professionals who seek maturity within their risk programs.