Risk Analysis最新文献

Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management.

Digital twins have become a popular and widely used tool for assessing risk and resilience, particularly as they have increased in the fidelity and accuracy of their representation of real-world systems. Although digital twins provide the ability to experiment on and assess risks to and from a system without damaging the real-world system, they pose potentially significant security risks. For example, if a digital twin of a power system has sufficient accuracy to allow loss of electrical power service due to a natural hazard to be estimated at the address level with a high degree of accuracy, what prevents someone wishing to lead to disruption at this same building from using the model to solve the inverse problem to determine which parts of the power system should be attacked to maximize the likelihood of loss of service to the target facility? This perspective article discusses the benefits and risks of digital twins and argues that more attention needs to be paid to the risks posed by digital twins.

Risk science is the most updated and justified knowledge-in the form of concepts, principles, approaches, methods, and models-for understanding, assessing, characterizing, communicating, and handling risk, with applications. It is also about the practice that gives us this knowledge. It is commonly stated that risk science is politically neutral. This perspective article discusses this assertion by scrutinizing the relationship between risk science and politics. In particular, it looks into the position of The Society for Risk Analysis (SRA) on this matter. Using some current risk problems as illustrating examples, including COVID-19 and climate change risk, the article argues that defending the political neutrality aim is paramount, especially with today's increasingly divisive political landscape.

The failure behavior of safety-critical systems typically depends on the system performance level, which offers opportunities to control system failure risk through dynamic performance adjustment. Moreover, mission abort serves as an intuitive way to mitigate safety hazards during mission execution. Our study focuses on systems that execute successive missions with random durations. To balance mission completion probability and system failure risk, we examine two decision problems: when to abort missions and how to select the performance level prior to mission abort. Our objective is to maximize the expected revenue through dynamic performance control and mission abort (PCMA) decisions. We consider condition-based PCMA decisions and formulate the joint optimization problem into a Markov decision process. We establish the monotonicity and concavity of the value function. Based on this insight, we show that optimizing the mission abort policy requires a series of control limits. In addition, we provide conditions under which the performance control policies are monotone. For comparative purposes, we analytically evaluate the performances of some heuristic policies. Finally, we present a case study involving unmanned aerial vehicles executing power line inspections. The results indicate the superiority of our proposed risk control policies in enhancing operational performance for safety-critical systems. Dynamic performance adjustment and mission abort decisions provide opportunities to reduce the failure risk and increase operational rewards of safety-critical systems.

The field of cyber risks is rapidly expanding, yet significant research remains to be conducted. Numerous taxonomy-based systems have been proposed in both the academic literature and industrial practice to classify cyber risk threats. However, the fragmentation of various approaches has resulted in a plethora of taxonomies, often incongruent with one another. In this study, we undertake a comprehensive review of these alternative taxonomies and offer a common framework for their classification based on their scope. Furthermore, we introduce desirable properties of a taxonomy, which enable comparisons of different taxonomies with the same scope. Finally, we discuss the managerial implications stemming from the utilization of each taxonomy class to support decision-making processes.

Earthquake insurance is a critical risk management strategy that contributes to improving recovery and thus greater resilience of individuals. Insurance companies construct premiums without taking into account spatial correlations between insured assets. This leads to potentially underestimating the risk, and therefore the exceedance probability curve. We here propose a mixed-effects model to estimate losses per ward that is able to account for heteroskedasticity and spatial correlation between insured losses. Given the significant impact of earthquakes in New Zealand due to its particular geographical and demographic characteristics, the government has established a public insurance company that collects information about the insured buildings and any claims lodged. We thus develop a two-level variance component model that is based on earthquake losses observed in New Zealand between 2000 and 2021. The proposed model aims at capturing the variability at both the ward and territorial authority levels and includes independent variables, such as seismic hazard indicators, the number of usual residents, and the average dwelling value in the ward. Our model is able to detect spatial correlation in the losses at the ward level thus increasing its predictive power and making it possible to assess the effect of spatially correlated claims that may be considerable on the tail of loss distribution.

As extreme weather events like floods and storms continue to increase, it is crucial to examine the degree to which various disaster preparedness and mitigation investments can lower these risks. In this research, we empirically examine the effects of multiple federal disaster aid programs on reducing subsequent flood- and storm-related damages across US coastal states. Our analysis distinguishes aid programs and their funded projects targeting different emergency management functions, including preparedness, nonstructural and structural mitigation, emergency response and protective measures, and rehabilitation of public infrastructure. We construct panel data of more than 1800 US counties over the years 2000-2019 and estimate a fixed-effects model with time-varying county-level socioeconomic and demographic characteristics. We find that disaster aid generally helps mitigate property damages, although this loss-reduction effect varies by program. Among all aid programs, the Emergency Management Performance Grant results in the largest reduction of future flood damages. The Public Assistance grants supporting emergency work are also found to exert a strong effect on risk reduction. We also find that the impacts of disaster aid are higher in coastal counties. Our study is one of the first few examining the resilience implication of disaster aid in coastal counties, and our results underscore the importance of investing in capacity building, contingency planning, and consistency in maintenance.

Approximately 80 million US adults-one in four-are infected with the human papillomavirus (HPV), which causes cancers of the cervix in women, cancers of the anus, penis, and throat in men, and genital warts in both sexes. Although HPV vaccinations are safe, effective, easily affordable, and readily available, a substantial percentage of parents resist recommendations to vaccinate their children against HPV. The current study tests the effects of different vaccination advocacy message strategies on attitudes toward HPV vaccination. Study participants (N = 963) were randomly assigned to one of four message conditions (a narrative story, an informational fact sheet, an appeal from an expert spokesperson, or an identical appeal from a nonexpert spokesperson) and assessed for change in attitude toward HPV vaccination along with levels of elaboration, narrative transportation, and freedom threat caused by the messages. Analyses showed that the messages' effects on attitude change were mediated by transportation and moderated by freedom threat. With the informative, expert, and nonexpert messages, increased message engagement produced increased freedom threat. With the narrative message, increased message engagement produced reduced levels of freedom threat. For risk communicators and planners of health interventions, the results suggest benefits for using a nonexpert advocacy message when levels of message engagement are expected to be low and using a story-based narrative advocacy message when levels of message engagement are expected to be high.

There are two separate conceptualizations for assessing existential risks: Planetary Boundaries (PBs) and global catastrophic risks (GCRs). While these concepts are similar in principle, their underpinning literatures tend not to engage with each other. Research related to these concepts has tended to be siloed in terms of the study of specific threats and also in terms of how these are assumed to materialize; PBs attribute global catastrophes to slow-moving and potentially irreversible global changes, while GCRs focuses on cataclysmic short-term events. We argue that there is a need for a more unified approach to managing global long-term risks, which recognizes the complex and confounded nature of the interactions between PBs and GCRs. We highlight where the PB and GCR concepts overlap and outline these complexities using an example of public health, namely, pandemics and food insecurity. We also present an existing indicator that we argue can be used for monitoring and managing risk. We argue for greater emphasis on national and global ''inclusive wealth'' as a way to measure economic activity and thus to monitor and mitigate the unintended consequences of economic activity. In sum, we call for a holistic approach to stewardship aimed at preserving the integrity of natural capital in the face of a broad range of global risks and their respective regional or global manifestations.

Flood models, while representing our best knowledge of a natural phenomenon, are continually evolving. Their predictions, albeit undeniably important for flood risk management, contain considerable uncertainties related to model structure, parameterization, and input data. With multiple sources of flood predictions becoming increasingly available through online flood maps, the uncertainties in these predictions present considerable risks related to property devaluation. Such risks stem from real estate decisions, measured by location preferences and willingness-to-pay to buy and rent properties, based on access to various sources of flood predictions. Here, we evaluate the influence of coastal flood predictions on real estate decision-making in the United Kingdom by adopting an interdisciplinary approach, involving flood modeling, novel experimental willingness-to-pay real estate surveys of UK residents in response to flood predictions, statistical modeling, and geospatial analysis. Our main findings show that access to multiple sources of flood predictions dominates real estate decisions relative to preferences for location aesthetics, reflecting a shift in demand toward risk averse locations. We also find that people do not consider flood prediction uncertainty in their real estate decisions, possibly due to an inability to perceive such uncertainty. These results are robust under a repeated experimental survey using an open access long-term flood risk map. We, therefore, recommend getting flood models "right" but recognize that this is a contentious issue because it implies having an error-free model, which is practically impossible. Hence, to reduce real estate risks, we advocate for a greater emphasis on effectively communicating flood model predictions and their uncertainties to non-experts.