Risk Analysis最新文献

The risk analysis community has long struggled with effectively addressing surprising events, primarily because the concept of surprise remains inadequately defined in the literature. Four common misconceptions about surprise continue to obstruct progress: (1) Surprise is a result of ignorance or lack of knowledge, (2) better predictions can help avoid surprise, (3) surprises can be eliminated, and (4) surprises are inherently adverse events that must be prevented. These misconceptions frame surprise as a problem of missing information, leading to an overemphasis on closing knowledge gaps rather than preparing for inevitable, unexpected disruptions. In this work, we offer a critical examination of surprise in the context of infrastructure resilience. We discuss the misconceptions surrounding surprise and propose a corrective framework that introduces surprise as an event that violates expectations followed by a series of cognitive reactions that lead to one of two intrinsic responses-either an adaptive response that involves updating the belief system through learning or a maladaptive (shock) response that reinforces outdated mental models and leaves the system vulnerable to future disruptions. We argue that understanding these responses is essential for improving the resilience of infrastructure systems, and we propose training programs to strengthen the adaptive capacities of infrastructure managers to shift the focus from attempting to eliminate surprise to embracing it as an opportunity for learning and adaptation.

Advances in artificial intelligence (AI) are reshaping mobility through autonomous vehicles (AVs), which may introduce risks such as technical malfunctions, cybersecurity threats, and ethical dilemmas in decision-making. Despite these complexities, the influence of consumers' risk preferences on AV acceptance remains poorly understood. This study explores how individuals' risk preferences affect their choices among private AVs (PAVs), shared AVs (SAVs), and private conventional vehicles (PCVs). Employing a lottery experiment and a self-reported survey, we first derive four parameters to capture individuals' risk preferences. Based on a stated preference experiment and the error component logit model, we analyze reference-dependent preferences for key attributes of PAVs and SAVs, using PCVs as the reference. Our analysis reveals that risk-tolerant consumers are more inclined toward PAVs or SAVs. Further, consumers exhibit a greater sensitivity to losses, such as higher purchasing prices and running costs, than to gains, such as reduced egress time. Specifically, for buying a PAV, consumers are willing to pay 3582 CNY more for 1000 CNY saving on annual running cost, 3470 CNY for a 1-min reduction in egress time, 28,880 CNY for removing driver liability for crashes, and 30,710 CNY for the improved privacy data security. For adopting SAVs, consumers are willing to pay 0.096 CNY extra per kilometer for a 1-min reduction in access time and 0.033 CNY extra per kilometer for a 1% increase in SAV availability. Therefore, this study enhances the understanding on risk preferences in AV acceptance and offers important implications for stakeholders in the AI-empowered mobility context.

Identifying critical risk factors is essential for controlling risk propagation and improving the safety management of carbon capture and storage (CCS) projects. Existing research has primarily focused on risk occurrence probability and potential consequences, with relatively less attention given to risk factor analysis, particularly their interactions within complex systems. To address this gap, 36 risk factors and 6 common accidents were identified through the literature review, analysis of accident reports, and expert interviews. We then established the CCS risk factor interaction network and identified critical structural nodes by topological analysis. To further examine the actual impact of these identified nodes and different parameters on risk propagation, we conducted a systematic simulation based on a susceptible-infected-recovered model. The results show that incomplete safety systems, inadequate safety supervision, and inadequate safety training serve as critical driving nodes, with a high potential to initiate widespread risk propagation, whereas equipment overload, adverse weather, and improper emergency handling act as critical bridge nodes whose intervention effectively suppresses risk propagation. Furthermore, the risk intervention step, propagation rate, and recovery rate affect the scale and duration of risk diffusion. This study aims to enhance system resilience by providing valuable insights for safety management in CCS projects.

Critical infrastructure is typically identified at the national level. However, disruption to certain infrastructure systems, facilities, and assets can have negative consequences for global societies. Such globally critical infrastructure entails a distinct risk profile for both countries dependent on the infrastructure, and countries that have such infrastructure in their territory. The goal of the article is to provide an initial framing and definition of "globally critical infrastructure" as a concept worthy of attention and explore the unique risk analysis and management challenges to support future, more rigorous examinations. For dependent countries, globally critical infrastructure exists outside of their border (or possibly outside any country's border), under sometimes drastically different economic, political, governance, and threat environments. Risk management entails unique challenges, because countries dependent on that infrastructure may have no legal or regulatory authority to shape risk management practices at facilities in other countries. Consequently, risk management may extend beyond the domains of the typical homeland or internal affairs agencies to include capabilities and responsibilities of ministries of foreign affairs, trade and commerce, and defense. However, those challenges also imply new risk management demands and options, such as new avenues for international cooperation on infrastructure protection and resilience, international funding, and enhanced monitoring. Having a globally critical infrastructure system in its borders changes the risk dynamics for a nation state, creating potential leverage over dependent nations and new avenues to garner international support, but also creates new risks to national sovereignty. Recognizing these common dependencies can better enable the global community to engage stakeholders to develop and implement systemic risk management approaches worldwide.

Hepatitis E virus (HEV) can lead to liver disease in humans. In the Netherlands, the consumption of pig meat is thought to be the main contributor to the total burden of disease caused by HEV. In this study, the number of cases and lost disability-adjusted-life-years (DALYs) due to HEV in pig meat were estimated by simulating HEV through the pig supply chain, including the farm, transport, lairage, slaughtering, processing, and consumption stages. The first four stages were modeled using a susceptible-exposed-infected-recovered (SEIR) model. For the last two stages, pig meat and liver products were divided into six product categories commonly consumed by Dutch consumers. Depending on the product category, different ways of heating and storing, leading to the reduction of infectious HEV genome copies, were assumed. Furthermore, the model was challenged by four selected control options at the pig farm: the cleaning of driving boards, the use of predatory flies, the use of rubber mats, and the vaccination of finishing pigs. Finally, the cost-effectiveness of these control measures was estimated by estimating the costs per avoided DALY. For the baseline situation, it was estimated that HEV in pig meat would lead to 70 cases and 21 DALYs per year. All control measures led to a decreased number of DALYs, with vaccination leading to the largest decrease: five DALYs per year. However, the costs per avoided DALY ranged from €0.5 to €7.5 million, making none of the control measures cost-effective unless the control measures are also effective against other pathogens.

Recent artificial intelligence (AI) risk management frameworks and regulations place stringent quality constraints on AI systems to be deployed in an increasingly competitive environment. Thus, from a software engineering point of view, a major issue is deciding when to release an AI system to the market. This problem is complex due to, among other features, the uncertainty surrounding the AI system's reliability and safety as reflected through its faults, the various cost items involved, and the presence of competitors. A novel general adversarial risk analysis framework with multiple agents of two types (producers and buyers) is proposed to support an AI system developer in deciding when to release a product. The implementation of the proposed framework is illustrated with an example and extensions to cases with multiple producers and multiple buyers are discussed.

We organized adversarial collaborations between subject-matter experts and expert forecasters with opposing views on whether recent advances in Artificial Intelligence (AI) pose an existential threat to humanity in the 21st century. Two studies incentivized participants to engage in respectful perspective-taking, to share their strongest arguments, and to propose early-warning indicator questions (cruxes) for the probability of an AI-related catastrophe by 2100. AI experts saw greater threats from AI than did expert forecasters, and neither group changed its long-term risk estimates, but they did preregister cruxes whose resolution by 2030 would sway their views on long-term risk. These persistent differences shrank as questioning moved across centuries, from 2100 to 2500 and beyond, by which time both groups put the risk of extreme negative outcomes from AI at 30%-40%. Future research should address the generalizability of these results beyond our sample to alternative samples of experts, and beyond the topic area of AI to other questions and time frames.

Fugitive dust from concentrated animal feeding operations (CAFOs) is a potential source of produce contamination with human pathogens. Our objective was to develop a general framework and methodology for predicting preharvest produce contamination with human pathogens arising from fugitive dust derived from a nearby CAFO. We applied this framework to a case study of lettuce grown in proximity to a bovine CAFO. We implemented the EPA's AERMOD dispersion model at two locations, assessing dust dispersion and deposition over a 30-day period across 100 km² surrounding a 10,000-animal CAFO. E. coli O157:H7 contaminated lettuce servings grown on fields within the 100 km² were predicted using a risk assessment approach, integrating data about dust deposition, pathogen contamination in cattle manure, and pathogen survival on crops. To contextualize the results, infectious servings were predicted based on the average number of E. coli O157:H7 per serving and the E. coli O157:H7 dose-response relationship. Dust from CAFOs has the potential to deposit across at least 100 km². E. coli O157:H7 dispersion and deposition are impacted by wind direction and velocity, emission factor, and prevalence and concentration in dust. Mean E. coli O157:H7 concentrations on preharvest lettuce were predicted across the 100 km² and declined considerably with distance from the CAFO. Surviving E. coli O157:H7 on preharvest lettuce arise primarily from dust deposited in the 2 weeks before harvest. Our modeling approach provides a flexible framework that can be adapted to any location, providing quantitative information to inform foodborne outbreak investigations and the development of prevention strategies.

Recent literature has examined the role of misinformation, biases, and other factors in contributing to the integrity of a risk study. These types of social and cognitive dynamics-referred to as narratives-comprise concern and value in a risk study. These narratives may appear to undermine aspects of objectivity in a scientific sense, but they may also shed light on aspects of a risk study that involve perceived scientific truths, related risk concerns, and values. The narratives can inform overall risk perception and the perception of quality for the risk study. As a result, understanding and classifying those narratives provides additional evidence that can potentially inform decisions for the design and implementation of a risk study. In this article, we develop a classification system that can be used to understand and address narratives that can influence a risk study and how various stakeholders perceive the risk study. This article will be of interest to risk analysts, policymakers, and risk communicators.

In this work, we introduce JointLIME, a novel interpretation method for explaining black-box survival (BBS) models with endogenous time-varying covariates (TVCs). Existing interpretation methods, like SurvLIME, are limited to BBS models only with time-invariant covariates. To fill this gap, JointLIME leverages the Local Interpretable Model-agnostic Explanations (LIME) framework to apply the joint model to approximate the survival functions predicted by the BBS model in a local area around a new individual. To achieve this, JointLIME minimizes the distances between survival functions predicted by the black-box survival model and those derived from the joint model. The outputs of this minimization problem are the coefficient values of each covariate in the joint model, serving as explanations to quantify their impact on survival predictions. JointLIME uniquely incorporates endogenous TVCs using a spline-based model coupled with the Monte Carlo method for precise estimations within any specified prediction period. These estimations are then integrated to formulate the joint model in the optimization problem. We illustrate the explanation results of JointLIME using a US mortgage data set and compare them with those of SurvLIME.