The Prioritization of Island Nations as Refuges from Extreme Pandemics

In this conceptual article with illustrative data, we suggest that it is useful to rank island nations as potential refuges for ensuring long-term human survival in the face of catastrophic pandemics (or other relevant existential threats). Prioritization could identify the several island nations that are most suitable for targeting social and political preparations and further investment in resiliency. We outline a prioritization methodology and as an initial demonstration, we then provide example rankings by considering 20 sovereign island states (all with populations greater than 250,000 and no land borders). Results describe each nation in nine resilience-relevant domains covering location, population, resources, and society according to published data. The results indicate that the most suitable island nations for refuge status are Australia, followed closely by New Zealand, and then Iceland, with other nations all well behind (including the relatively high-income ones of Malta and Japan). Nevertheless, some key contextual factors remain relatively unexplored. These include the capacity of the jurisdiction to rapidly close its borders when the emerging threat was first detected elsewhere, and whether or not large subnational islands should be the preferred focus for refuge design (e.g., the Australian state of Tasmania, the island of Hokkaido in Japan, or the South Island of New Zealand). Overall, this work provides conceptual thinking with some initial example analysis. Further research could refine the selection of metrics, how best to weight the relevant domains, and how the populations of prioritized island nations view their nation's selection as a potential refuge for human survival.     

Health insurance system and resilience to epidemics

We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID-19. Our findings can be summarized as follows. First, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. The decentralized behavior of competing insurers leads to a less resilient outcome. Second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. Third, a higher contagion rate, a more cost-efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. Fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction.     

Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty

The design and planning of resilient supply chains is a major challenge due to the increasing complexity of these systems that operate in a global market and therefore are more exposed to disruptions. In the present work a design and planning model that integrates demand uncertainty is applied to five supply chain structures that are submitted to different types of disruptions. Disruptions are modelled in a probabilistic manner, resulting in the incorporation of two sources of uncertainty. Eleven indicators are considered to assess the supply chains’ resilience, which comprise network design, centralization and operational indicators. The goal is to provide managers what are expected operational impacts (measured by the operational indicators) by assessing the behavior of network and centralization indicators and their known resilience behaviors from the literature. A case study of a European supply chain is used to illustrate the methodology and a discussion on the results obtained is presented in order to conclude which main characteristics a manager should consider when designing and planning resilient supply chains.     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems

Recent natural and man‐made catastrophes, such as the Fukushima nuclear power plant, flooding caused by Hurricane Katrina, the Deepwater Horizon oil spill, the Haiti earthquake, and the mortgage derivatives crisis, have renewed interest in the concept of resilience, especially as it relates to complex systems vulnerable to multiple or cascading failures. Although the meaning of resilience is contested in different contexts, in general resilience is understood to mean the capacity to adapt to changing conditions without catastrophic loss of form or function. In the context of engineering systems, this has sometimes been interpreted as the probability that system conditions might exceed an irrevocable tipping point. However, we argue that this approach improperly conflates resilience and risk perspectives by expressing resilience exclusively in risk terms. In contrast, we describe resilience as an emergent property of what an engineering system does, rather than a static property the system has. Therefore, resilience cannot be measured at the systems scale solely from examination of component parts. Instead, resilience is better understood as the outcome of a recursive process that includes: sensing, anticipation, learning, and adaptation. In this approach, resilience analysis can be understood as differentiable from, but complementary to, risk analysis, with important implications for the adaptive management of complex, coupled engineering systems. Management of the 2011 flooding in the Mississippi River Basin is discussed as an example of the successes and challenges of resilience‐based management of complex natural systems that have been extensively altered by engineered structures.     

基于线性分段恢复函数的基础设施韧性分析模型——以C县电力系统网络为例

针对近年来一系列突发事件冲击和破坏着城市关键基础设施系统的正常运行,并造成了较为严重的社会后果的现实问题,提出了如何保护关键基础设施系统的研究问题,以使基础设施系统能够对灾害情景做出迅速的响应,并迅速地处理以恢复到常态。本研究基于三种典型的恢复函数提出了线性分段恢复函数,构建了关键基础设施系统韧性分析模型,并用蒙特卡洛模拟的方法应用到C县的电力系统网络加以验证,得到了该韧性分析模型不仅可以帮助决策者在灾害情境下权衡预算成本和韧性的关系,也可以识别关键基础设施系统网络中需要保护的关键节点,从而实现对关键基础设施系统的针对性保护的结论。本研究构建的韧性分析模型有为灾害情境下对电力系统采取针对性保护的现实价值,和开拓了对基础设施系统进行保护研究的分析模型的理论价值。     

Detecting nuclear materials smuggling: performance evaluation of container inspection policies

In recent years, the United States, along with many other countries, has significantly increased its detection and defense mechanisms against terrorist attacks. A potential attack with a nuclear weapon, using nuclear materials smuggled into the country, has been identified as a particularly grave threat. The system for detecting illicit nuclear materials that is currently in place at U.S. ports of entry relies heavily on passive radiation detectors and a risk-scoring approach using the automated targeting system (ATS). In this article we analyze this existing inspection system and demonstrate its performance for several smuggling scenarios. We provide evidence that the current inspection system is inherently incapable of reliably detecting sophisticated smuggling attempts that use small quantities of well-shielded nuclear material. To counter the weaknesses of the current ATS-based inspection system, we propose two new inspection systems: the hardness control system (HCS) and the hybrid inspection system (HYB). The HCS uses radiography information to classify incoming containers based on their cargo content into "hard" or "soft" containers, which then go through different inspection treatment. The HYB combines the radiography information with the intelligence information from the ATS. We compare and contrast the relative performance of these two new inspection systems with the existing ATS-based system. Our studies indicate that the HCS and HYB policies outperform the ATS-based policy for a wide range of realistic smuggling scenarios. We also examine the impact of changes in adversary behavior on the new inspection systems and find that they effectively preclude strategic gaming behavior of the adversary.     

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.     

A Risk-Based Method for Modeling Traffic Fatalities

We describe a risk-based analytical framework for estimating traffic fatalities that combines the probability of a crash and the probability of fatality in the event of a crash. As an illustrative application, we use the methodology to explore the role of vehicle mix and vehicle prevalence on long-run fatality trends for a range of transportation growth scenarios that may be relevant to developing societies. We assume crash rates between different road users are proportional to their roadway use and estimate case fatality ratios (CFRs) for the different vehicle-vehicle and vehicle-pedestrian combinations. We find that in the absence of road safety interventions, the historical trend of initially rising and then falling fatalities observed in industrialized nations occurred only if motorization was through car ownership. In all other cases studied (scenarios dominated by scooter use, bus use, and mixed use), traffic fatalities rose monotonically. Fatalities per vehicle had a falling trend similar to that observed in historical data from industrialized nations. Regional adaptations of the model validated with local data can be used to evaluate the impacts of transportation planning and safety interventions, such as helmets, seat belts, and enforcement of traffic laws, on traffic fatalities.     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

The Vulnerability of the Nevada Visitor Economy to a Repository at Yucca Mountain

This article reviews the studies commissioned by the Nevada Nuclear Waste Project Office to estimate the economic impact of a high-level nuclear waste repository at Yucca Mountain. Case studies found that visitor impacts occur for some analogous facilities, but not for others. Assessments of behavioral intent indicate that at least some economic agents would avoid visiting Nevada under repository scenarios. A third set of studies tested the risk-aversion and negative-imagery models of visitor decision making; people avoid visiting places associated with either a significant health risk or negative imagery, but it has yet to be shown that a repository would induce these perceptions in nearby places. In sum, the NWPO-sponsored studies suggest the potential for visitor impacts, but do confirm that these effects will occur.     

Physical–cyber–human framework-based resilience evaluation toward urban power system: Case study from China

Because the increased frequency, intensity, and duration of extreme weather events have significantly challenged power systems, there has been an increased interest in resilient power systems. This article establishes a multicriteria resilience evaluation framework for urban power systems from a physical–cyber–human system perspective, in which the two principal elements responsible for power system function degradation are described, the three major domains comprising urban power systems are explained, four core capacities that positively contribute to power system resilience are proposed, and 15 (11 objective and four subjective) power system resilience evaluation indicators are identified. Fuzzy hesitant judgment and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) aggregation method are employed to minimize the expert divergence and maximize the group consensus. A validation method is designed and a comparison with commonly applied performance-based and attributes-based evaluation methods is conducted. The applicability of the evaluation framework is verified using data from four Chinese municipalities: Shanghai, Beijing, Chongqing, and Tianjin. It was found that Shanghai's resilience was the best, and Chongqing's physical resistance disadvantages would result in the greatest difficulties in coping with extreme event disturbances. Physical, cyber, and human domain resilience enhancement strategies are given for different cities separately. This study provides a practical tool to evaluate, compare, and enhance power system resilience for governments and public utilities.     

Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.     

A dynamic perspective on the resilience of firms: A systematic literature review and a framework for future research

This study aims to answer the following research question: how is the resilience of firms defined in the business and management field? In doing so, we answer recent calls for research about a more thorough conceptualisation of the resilience of firms and its definition. We conducted a systematic literature review of 66 selected papers published between 2000 and 2017. By means of inductive content analysis, we analyse the definitions of ‘resilience’ and elaborate a novel conceptual framework that introduces a dynamic perspective on the resilience of firms. The proposed framework overcomes existing definitional fragmentation and raises awareness of the temporal dimension in the conceptualisation of the resilience of firms. We contribute to extant business and management literature on the resilience of firms by proposing a model that articulates two main paths for explaining organisational resilience, i.e. absorptive resilience and adaptive resilience paths. We also identify a set of key capabilities needed to be successfully resilient at the different stages of the two paths.     

A dynamic Bayesian network-based emergency decision-making framework highlighting emergency propagations: Illustrated using the Fukushima nuclear accidents and the Covid-19 pandemic

When facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. However, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. In the work reported here, we develop a framework based upon dynamic Bayesian networks in order to simulate emergency scenarios and support corresponding decisions. In this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. We illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. By incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. Scenarios developed from two public emergencies: the 2011 Fukushima nuclear power plant accidents and the Covid-19 pandemic, are utilized to illustrate the framework in this paper. Capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real-world situations. Our analysis results reveal many similarities between these two seemingly distinct events. This indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. Valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically.     

Human resource development (HRD) resilience: a new ‘success element’ of organizational resilience?

ABSTRACT

Endlessly changing business and economic landscapes urge organizations to become resilient to ensure business survival and growth. Yet, in many cases, business world is becoming turbulent faster than organizations are becoming resilient. Relevant research indicates the ways through which organizations could respond to unforeseen events, mainly through suggesting that individual and group resilience could lead to an organizational one. However, research is nascent on how particularly human resource development (HRD) resilience could be built, and thus to contribute to organizational resilience as well. Within today’s business uncertainty and complexity, HRD resilience comes in line with the developmental strategies of organizations. Therefore, the purpose of this perspective article is to set the foundations of the term (HRD resilience) in order to initiate a dialogue around its ability to make a substantial contribution to organizational practice, and thus to be seen as a new ‘success element’ of organizational resilience.     

The Effectiveness of Risk Management: An Analysis of Project Risk Planning Across Industries and Countries

This article examines the effectiveness of current risk management practices to reduce project risk using a multinational, multi‐industry study across different scenarios and cultures. A survey was administered to 701 project managers, and their supervisors, in seven industries and three diverse countries (New Zealand, Israel, and Japan), in multiple languages during the 2002–2007 period. Results of this study show that project context—industry and country where a project is executed—significantly impacts perceived levels of project risk, and the intensity of risk management processes. Our findings also suggest that risk management moderates the relationship between risk level and project success. Specifically, we found that even moderate levels of risk management planning are sufficient to reduce the negative effect risk levels have on project success.     

Morality and Nuclear Energy: Perceptions of Risks and Benefits,Personal Norms,and Willingness to Take Action Related to Nuclear Energy

We examined factors underlying people's willingness to take action in favor of or against nuclear energy from a moral perspective. We conducted a questionnaire study among a sample of the Dutch population (N = 123). As expected, perceptions of risks and benefits were related to personal norms (PN), that is, feelings of moral obligation toward taking action in favor of or against nuclear energy. In turn, PN predicted willingness to take action. Furthermore, PN mediated the relationships between perceptions of risk and benefits and willingness to take action. In line with our hypothesis, beliefs about the risks and benefits of nuclear energy were less powerful in explaining PN for supporters compared to PN of opponents. Also, beliefs on risks and benefits and PN explained significantly more variance in willingness to take action of opponents than of supporters. Our results suggest that a moral framework is useful to explain willingness to take action in favor of and against nuclear energy, and that people are more likely to protest in favor of or against nuclear energy when PN are strong.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.     

Nuclear Waste Management under Approaching Disaster: A Comparison of Decommissioning Strategies for the German Repository Asse II

This article compares different strategies for handling low‐ and medium‐level nuclear waste buried in a retired potassium mine in Germany (Asse II) that faces significant risk of uncontrollable brine intrusion and, hence, long‐term groundwater contamination. We survey the policy process that has resulted in the identification of three possible so‐called decommissioning options: complete backfilling, relocation of the waste to deeper levels in the mine, and retrieval. The selection of a decommissioning strategy must compare expected investment costs with expected social damage costs (economic, environmental, and health damage costs) caused by flooding and subsequent groundwater contamination. We apply a cost minimization approach that accounts for the uncertainty regarding the stability of the rock formation and the risk of an uncontrollable brine intrusion. Since economic and health impacts stretch out into the far future, we examine the impact of different discounting methods and rates. Due to parameter uncertainty, we conduct a sensitivity analysis concerning key assumptions. We find that retrieval, the currently preferred option by policymakers, has the lowest expected social damage costs for low discount rates. However, this advantage is overcompensated by higher expected investment costs. Considering all costs, backfilling is the best option for all discounting scenarios considered.