Risk Analysis

On April 28, 2004, the United Nations Security Council unanimously adopted United Nations Security Council Resolution (UNSCR) 1540. It requires countries to develop and enforce legal and regulatory measures against the proliferation of weapons of mass destruction (WMDs) and their means of delivery, with a focus on the spread to nonstate actors. To date, compliance with UNSCR 1540 has been challenging. Data included in the UNSCR 1540 Committee 2016 report indicate that approximately 35 countries, or 18% of the UN member states, have implemented 70% of the Resolution's requirements. This article uses a multimethod approach to evaluate compliance with UNSCR 1540, including key‐word analysis of existing literature to identify compliance factors and a quantitative evaluation method, based on weighting and scoring of these factors by the authors. The model was vetted by a panel of experts and tested on a sample of 12 countries showing that the compliance scores derived from the model correspond to the experts’ wholistic judgments about compliance and agreement with the scores of more complex models.

The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and AI. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how AI can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.

This study explores the risk management challenges associated with safety‐critical systems required to execute specific missions. The working component experiences degradation governed by a continuous‐time discrete‐state Markov chain, whose failure leads to an immediate system breakdown and safety losses. To enhance system survivability, a limited number of identical spares are available for online replacement throughout the mission. At the same time, the mission abort action arises promptly upon encountering excessive safety hazards. To strike an optimal balance between mission completion and system survivability, we delve into the adaptive scheduling of component replacements and mission termination decisions. The joint decision problem of interest constitutes a finite‐time Markov decision process with resource limitation, under which we analyze a series of structural properties related to spare availability and component conditions. In particular, we establish structured control‐limit policies for both spare replacement and mission termination decisions. For comparison purposes, we evaluate the performance of various heuristic policies analytically. Numerical experiments conducted on the driver system of radar equipment validate the superior model performance in enhancing operational performance while simultaneously mitigating hazard risks.

The link between environmental threats and adverse consequences is rarely observable due to delayed consequences. Some scholars have therefore argued that decreasing the psychological distance could increase motivation to engage in proenvironmental behavior. Support for this argument has so far been mixed for climate change, perhaps because behavioral changes to mitigate climate change requires significant changes in lifestyle. This study presents a parallel line of investigation into another environmental issue, where behavioral changes are less invasive, namely, pesticides. Each of the four dimensions of psychological distances on pesticides is manipulated in a survey experiment. The findings suggest that while proximizing can, in some cases, enhance risk perception and promote behavioral intentions, the effect remains weak, supporting the argument that applying construal level theory (CLT) for predicting the effect of psychological distance on environmental issues is an overextension of the theory's original scope, also for issues other than climate change.

This article presents a planning framework to improve the weather‐related resilience of natural gas–dependent electricity distribution systems. The problem is formulated as a two‐stage stochastic mixed integer linear programing model. In the first stage, the measures for distribution line hardening, gas‐fired distributed generation (DG) placement, electrical energy storage resource allocation, and tie‐switch placement are determined. The second stage minimizes the electricity distribution system load shedding in realized hurricanes to achieve a compromise between operational benefits and investment costs when the dependence of electricity distribution system on the natural gas exists. The proposed stochastic model considers random failures of electricity distribution system lines and random errors in forecasting the load demand. The Monte Carlo simulation is employed to generate multiple scenarios for defining the uncertainties of electricity distribution system. For the sake of simplicity, weather‐related outages of natural gas pipelines are considered deterministic. The nonlinear natural gas constraints are linearized and incorporated into the stochastic optimization model. The proposed framework was successfully implemented on an interconnected energy system composed of a 33‐bus electricity distribution system and a 14‐node natural gas distribution network. Numerical results of the defined case studies and a devised comparative study validate the effectiveness of the proposed framework as well.

Increasing awareness of climate change and its potential consequences on financial markets has led to interest in the impact of climate risk on stock returns and portfolio composition, but few studies have focused on perceived climate risk pricing. This study is the first to introduce perceived climate risk as an additional factor in asset pricing models. The perceived climate risk is measured based on the climate change sentiment of the Twitter dataset with 16 million unique tweets in the years 2010–2019. One of the main advantages of our proxy is that it allows us to capture both physical and transition climate risks. Our results show that perceived climate risk is priced into Standard and Poor's 500 (S&P 500) Index stock returns and is robust when different asset‐pricing models are used. Our findings have implications for market participants, as understanding the relationship between perceived climate risk and asset prices is crucial for investors seeking to navigate the financial implications of climate change and for policymakers aiming to promote sustainable financing and mitigate the potential damaging effects of climate risk on financial markets, and a pricing model that accurately incorporates perceived climate risk can facilitate this understanding.

Information is crucial for risk management; however, no quantified measure to evaluate risk information exists to date. The standard measure of value of factual information is information entropy—that is, the negative logarithm of probability. Despite its applications in various fields, this measure is insufficient for the evaluation of risk information; there are three reasons. First, it requires precise probabilities, which are generally absent in the context of risks. Second, it does not consider the effect of the consequences, which is essential for risks. Third, it does not account for human preferences and subjectivity. This study proposes a quantified measure for the evaluation of factual risk information—that is, observations of occurrence, particularly for binary, unambiguous, and rare phenomena. To develop such a measure, precise probabilities are replaced with updated probabilities, based on the Prospective Reference Theory. Additionally, utility is included as a proxy for the size of consequences. The third challenge—human preferences and subjectivity—is partly addressed by the application of updated perceived probabilities and utility as a measure of human preferences. Such a conventional, quantified measure facilitates the comparison of the potential impact of different messages for a new observation of occurrence for a risk, as well as of messages for different risks. Moreover, it clarifies the factors that systematically affect this impact. More particularly, it indicates the major effects of the perceived number of past occurrences.

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.

Multifarious applications of unmanned aerial vehicles (UAVs) are thriving in extensive fields and facilitating our lives. However, the potential third‐party risks (TPRs) on the ground are neglected by developers and companies, which limits large‐scale commercialization. Risk assessment is an efficacious method for mitigating TPRs before undertaking flight tasks. This article incorporates the probability of UAV crashing into the TPR assessment model and employs an A* path‐planning algorithm to optimize the trade‐off between operational TPR cost and economic cost, thereby maximizing overall benefits. Experiments demonstrate the algorithm outperforms both the best‐first‐search algorithm and Dijkstra's algorithm. In comparison with the path with the least distance, initially, the trade‐off results in a 1.88%$1.88\%$ increase in distance while achieving an 89.47%$89.47\%$ reduction in TPR. As the trade‐off progresses, this relationship shifts, leading to a 20.62%$20.62\%$ reduction in the distance with only a negligible increase in TPR by 0.0001, matching the TPR‐cost‐based algorithm. Furthermore, we conduct simulations on the configuration of UAV path networks in five major cities in China based on real‐world travel data and building data. Results reveal that the networks consist of one‐way paths that are staggered in height. Moreover, in coastal cities particularly, the networks tend to extend over the sea, where the TPR cost is trivial.

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.

In recent years, “black swan” events have increasingly occurred across climate, epidemics, geopolitics, and economics, leading to a gradual coupling of different types of risk. Different from isolated shocks as a single type of risk affecting a specific industry, a nexus of risks allows one risk area to quickly relate to others, resulting in more catastrophic impacts. Utilizing an integrated framework, we investigate the contagion effects among climate policy uncertainty, the infectious disease equity market volatility tracker, geopolitical risk, and economic policy uncertainty using volatility, skewness, and kurtosis as risk measures. The results indicate that: (1) The contagion effect of different types of risk increases with higher order risk measures, suggesting that more extreme events are more likely to be contagious across domains. (2) Approximately two‐thirds of risk contagion occurs contemporaneously, while about one‐third occurs with a lag, indicating that risk contagion combines both immediacy and continuity. (3) Risk contagion exhibits significant time‐varying and heterogeneous characteristics. Our study elucidates the inherent contagion characteristics between different types of risk, transforming the understanding of risk from a one‐dimensional to a multidimensional perspective. This underscores that risk management should not be confined to a single domain; it is crucial to consider the potential impacts of risks from other industries on one's own.

Community resilience measurement to natural hazards is becoming increasingly relevant due to the growth of federal programs and local and state resilience offices in the United States. This study introduces a methodology to co‐produce an actionable resilience metric to measure locally relevant and modifiable indicators of community resilience for the state of South Carolina. The “actionable” metrics, based on the Baseline Resilience Indicators for Communities (BRIC) index, are calculated at the county and tract scale and then compared to “conventional” versions of BRIC. Actionable BRICs perform better in reliability testing than conventional BRICs. Correlations across the two scales of BRIC construction show a stronger relationship between the actionable BRICs than conventional, though all are highly correlated. When mapped, actionable BRIC shows a shifted region of low resilience in the state when compared to conventional BRIC, suggesting that actionable and conventional BRICs are distinct. Scale differences show dissimilar drivers of resilience, with county‐level resilience driven by community, social, and environmental resilience and tract‐level resilience driven by social and institutional resilience. Actionable tract‐level BRIC appears to be the best representation of modifiable resilience for South Carolina, but it comes with trade‐offs, including calculation complexity and changing geographies over time. Regardless of scale, the resulting actionable indices offer a useful tracking mechanism for the state resilience office and highlight the importance of integrating top‐down and bottom‐up resilience perspectives to consider local drivers of resilience. The resulting methodology can be replicated in other states and localities to produce actionable and locally relevant resilience metrics.

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial‐allocation, spatial‐extent, temporal‐only, and spatial–temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial‐allocation and spatial–temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade‐offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long‐term impacts. Enhancing these models' accuracy and applicability will enable decision‐makers to better manage wildfire treatment outcomes.

In light of recent events related to national elections in the United States, safeguarding the security and integrity of forthcoming elections stands as a critical national priority. Elections equipment in the United States constitutes critical national infrastructure, and its operation relies on poll workers, who are trusted insiders. However, those insiders may pose risks if they make mistakes with detrimental consequences or act with malice. This research analyzes a large dataset of 2213 responses obtained from a survey of poll workers and potential poll workers in 13 states. The survey includes the Security Behavior Intentions Scale, which has been previously established and validated in the security literature. We use the responses to assess poll workers’ intentions of complying with established security‐related practices. We develop a novel model using information theory to examine potential weaknesses in security behaviors and identify poll worker security practices to improve to ensure the integrity of our elections. We also recommend action items and countermeasures for states and localities based upon this empirical analysis.

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.

Over the last years, infectious diseases have been traveling across international borders faster than ever before, resulting in major public health crises such as the Covid‐19 pandemic. Given the rapid changes and unknown risks that mark such events, risk communication faces the challenge to raise awareness and concern among the public without creating panic. Drawing on the social amplification of risk framework—a concept that theorizes how and why risks are amplified or attenuated during the (1) transfer of risk information (by, for instance, news media) and (2) audiences’ interpretation and perception of these information—we were interested in the portrayal of risk information and its impact on audiences’ risk perception over the first wave of the Covid‐19 pandemic in Germany. We therefore conducted a quantitative content analysis of a major public and private television (TV) newscast (N = 321) and combined it with survey data (two‐wave panel survey, t1: N = 1378 and t2: N = 1061). Our results indicate that TV news (as a major information source at that time) were characterized by both risk‐attenuating and risk‐amplifying characteristics, although risk‐amplifying attributes were particularly pronounced by the private TV newscast. Notably, those who only used private TV news between both waves showed the highest perceived severity at time 2. However, the interaction effect of time and use of public and/or private TV news was only significant for perceived susceptibility. Overall, more research is needed to examine the effects of different types of media and changes in risk perceptions over time.

As synthetic biology is extensively applied in numerous frontier disciplines, the biosafety and biosecurity concerns with designing and constructing novel biological parts, devices, and systems have inevitably come to the forefront due to potential misuse, abuse, and environmental risks from unintended exposure or potential ecological impacts. The International Genetically Engineered Machine (iGEM) competition often serves as the inception of many synthetic biologists’ research careers and plays a pivotal role in the secure progression of the entire synthetic biology field. Even with iGEM's emphasis on biosafety and biosecurity, continuous risk assessment is crucial due to the potential for unforeseen consequences and the relative inexperience of many participants. In this study, possible risk points for the iGEM projects in 2022 were extracted. An attack tree that captures potential risks and threats from experimental procedures, ethical issues, and hardware safety for each iGEM‐based attack scenario is constructed. It is found that most of the attack scenarios are related to experimental procedures. The relative likelihood of each scenario is then determined by using an established assessment framework. This research expands the traditionally qualitative analysis of risk society theory, reveals the risk formation in the synthetic biology team, and provides practical implications.

This study examined the paths through which the news frames of particulate matter (PM) influence support for governmental policies aiming to address PM. It also explored the mediating effects of anxiety and risk perception in the relationship between news frames and policy support, as well as the moderating effects of media exposure and psychological distance on the PM news framing effect. Based on an experimental design (N = 676), two groups of news frames were prepared for comparison: a narrative frame group and a numerical frame group. The results showed no significant differences in anxiety or risk perception between the two groups. Further, no significant mediating effects of anxiety or risk perception were found in the process through which PM news frames influence support for governmental policies. However, media exposure significantly moderated the effect of the narrative frame: With high (low) media exposure, the narrative frame positively (negatively) influenced policy support through risk perception. Moreover, when the level of psychological distance was low, the narrative frame positively influenced policy support through risk perception. This study contributes to the literature on news framing of PM by integrating cognitive and emotional mechanisms in forming policy attitudes.

The communication of extreme weather forecasts (e.g., heatwaves and extreme precipitation) is a challenge for weather forecasters and emergency managers who are tasked with keeping residents safe during often unprecedented situations. Weather models have inherent uncertainty, and the ability for potentially life‐saving information to get to the people who need it most (e.g., those who need to evacuate) remains a challenge despite the proliferation of digital access to information and social media sites like Twitter. It is also unclear the role that community‐based organizations and super‐local governmental entities play or may play during weather events in transmitting weather information and providing assistance. In New York City, there remains robust inequality, with communities that are historically disadvantaged often suffering the highest number of deaths and level of destruction following weather events. Results from interviewing 26 New York City community leaders suggest that local organizations often act as intermediaries, passing on official weather information to members of their audience, regardless of the mission statement of their organization. Common challenges for communities in responding to extreme weather include lack of access to information, language barriers, and insufficient resources. Considerations for future weather communication strategies are discussed.

SARS‐CoV‐2 Omicron and its sub‐lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision‐makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short‐term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS‐CoV‐2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.

The proliferation of inaccurate and misleading information about COVID‐19 on social media poses a significant public health concern. This study examines the impact of the infodemic and beneficial information on COVID‐19 protective behaviors in an armed‐conflict country. Using the protective action decision model (PADM), data were collected from 1439 participants through a questionnaire in Yemen between August 2020 and April 2021. Structural equation modeling tested hypotheses generated by the PADM. The findings indicate that the infodemic reduces the likelihood of individuals adopting protective measures against COVID‐19. Surprisingly, official announcements by accountable authorities do not moderate the relationship between the infodemic and protective responses. These results highlight the need for further research on resilience in armed‐conflict countries. This study contributes to understanding armed‐conflict countries' unique challenges in combating health crises. Addressing the infodemic and promoting accurate information is crucial in enhancing protective behaviors and mitigating the negative impact of misinformation. Policymakers and public health authorities can utilize these insights to develop targeted interventions and communication strategies that ensure accurate information dissemination and encourage the adoption of adequate protective measures.

The concept of resilience intrinsically links with both complexity and adaptive capacity. Scholars from different fields agree on this. Still, the detailed relations between resilience, complexity, and adaptive capacity need a more thorough theoretical analysis. This article analyses resilience with the help of assumptions from complex adaptive systems (CAS) theory to answer two questions in more detail: What is the relation between resilience and complexity? How can adaptive capacity contribute to resilience? By applying basic ideas from CAS theory to the resilience discourse, the article deduces that complexity of a system is a necessary condition for resilience because complex systems consist of agents that possess adaptive capacity, whereas simple systems consist of mere elements that cannot adapt to unexpected disruptions. The relation between complexity and resilience is multidimensional. Growing complexity leads to a growing need for resilience because the chances for severe, unexpected disruptions increase. The analysis of adaptive capacities revealed that systems and the agents they consist of can possess of specialized and general adaptive capacity. General adaptive capacity is the core feature of resilience because it enables systems to cope with unexpected disruptions. System design principles such as diversity within functional groups and redundancy help to increase general adaptive capacity. The same is true on the community level for social capital and on the individual level for disaster preparedness measures because they increase coping capacities independent of specific hazards.

Although persuasive messages are designed to motivate individuals to engage in intended behaviors, they do not always work. Often, people follow previously established values and ideologies and dismiss persuasive messages. We examine how participants react to a persuasive message related to plastic pollution and how these reactions shape their willingness to recycle and reuse. Results indicate that environmental values and political ideology are associated with message derogation in distinct ways, which, in turn, affect risk perception, self‐efficacy, and intention to recycle and reuse. Further, past behavior moderates the relationship between message derogation and perceived risk, but not the relationship between message derogation and self‐efficacy. These results suggest that pre‐existing values and ideologies play an important role in message derogation, a hitherto under‐researched phenomenon that has key implications for self‐reported behavioral change. Moreover, past behavior could serve as a powerful lever in steering risk perception and behavioral intent.