Risk Analysis最新文献

We offer an alternative approach to toxicological risk assessment of new chemicals. We combine Operations Research techniques with those from Machine Learning to tackle the decision-making process. More specifically, we use Markov decision processes and Bayesian networks to derive the optimal cost-sensitive time-efficient Integrated Testing Strategies for chemical hazard classification under minimal expected cost in a mathematically rigorous fashion. We develop Bayesian networks which outperform state-of-the-art mechanistic causal models previously reported. More specifically, these models exhibit accuracy of 90% and sensitivity and specificity of 93% and 84%, respectively. Moreover, the inferred Bayesian networks are of considerably simpler structure as they comprise only the permeation coefficient, octanol/water coefficient, and TIMES software compared to their counterparts already in print, which comprise 15 descriptors. We use these simplified causal models to study the effect of varying misclassification costs on the nature of the optimal policy by means of sensitivity analysis. We note such analysis was previously computationally infeasible due to the fact that the variables which comprised the mechanistic model were categorical assuming a large number of possible values. We find that a variety of optimal policies can emerge subject to different misclassification costs assumed. Theoretical modeling framework developed is illustrated on the concrete example of hazard classification of skin allergens of previously unknown toxicological characteristics via integrating data obtained from in silico assays alone thus contributing to the literature of toxicological decision making based on nonanimal tests.

In recent years, frequent extreme disasters have challenged supply chain operations while smart risk warning systems are developed to facilitate firms' emergency order shifting to a new manufacturer. It is noted that reliable manufacturers are usually located in countries/regions levying carbon tax to achieve high ESG scores, so we consider a cross-border supply chain consisting of a global brand, a local brand, an overseas manufacturer and a local manufacturer to investigate the main tradeoffs for the global brand to emergently shift orders from the overseas manufacturer facing disruptions to a stable local manufacturer subject to carbon tax cost. The global brand has the option to wait for the recovery of overseas production but if it chooses emergent order shifting, it has to invest in carbon emission reduction due to ESG requirements. We intriguingly find that even though emergency order shifting helps avert delays caused by production disruptions, a more resilient supply chain does not necessarily lead to a higher profit for the global brand, depending on factors such as the relative market size, carbon tax cost, and the efficiency of carbon reduction investment. We also find that the global brand's emergency order shifting enables Pareto improvement of economic and environmental sustainability, but the win-win opportunities for both the global and local brand only appear under the recovery waiting strategy. So it is generally hard to coordinate the stakeholders' incentives to jointly optimize the ESG scores.

The use of AI technologies is being integrated into the secure development of software-based systems, with an increasing trend of composing AI-based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and seriously threatens safety-critical domains. Despite the existing knowledge about uncertainty in risk analysis, no previous work has estimated the uncertainty of AI-augmented systems given the propagation of errors in the pipeline. We provide the formal underpinnings for capturing uncertainty propagation, develop a simulator to quantify uncertainty, and evaluate the simulation of propagating errors with one case study. We discuss the generalizability of our approach and its limitations and present recommendations for evaluation policies concerning AI systems. Future work includes extending the approach by relaxing the remaining assumptions and by experimenting with a real system.

We propose a novel dynamic generalized Pareto distribution (GPD) framework for modeling the time-dependent behavior of the peak over threshold (POT) in extreme smog (PM_2.5) time series. First, unlike static GPD, three dynamic autoregressive conditional generalized Pareto (ACP) models are introduced. Specifically, in these three dynamic models, the exceedances of air pollutant concentration are modeled by a GPD with time-dependent scale and shape parameters conditioned on past PM_2.5 and other air quality factors (SO₂, NO₂, CO) and weather factors (daily average temperature, average relative humidity, average wind speed). Second, unlike the recent studies of ACP models, we impose a logistic function autoregressive structure on the scale and shape parameters of the ACP models, which has simple calculation and flexible modeling for the scale and shape parameters, since the logistic function is used to mean that the changes in the long memory parameter occur in a continuous manner and often applied in time series models. Third, the model averaging method is applied to improve predictive performance using AIC and BIC criteria to select combined weights of the three ACP models. In addition, based on goodness-of-fit tests, the thresholds of the three ACP models are chosen by eight automatic threshold selection procedures to avoid subjectively assigning a certain value as the threshold. Maximum likelihood estimation (MLE) is employed to estimate parameters of the ACP models and its statistical properties are investigated. Various simulation studies and an example of real data in PM_2.5 time series demonstrate the superiority of the proposed ACP models and the stability of the MLE.

In an era of increasing digitization, nuclear command systems and power plants are becoming vulnerable to cyberattacks that can disrupt operations and undermine deterrence. This article examines the evolving threat landscape, drawing on literature and documented incidents of cyber intrusions into nuclear systems to identify critical technical and policy vulnerabilities. It argues that such intrusions risk eroding second-strike credibility and may create incentives for preemptive action, thereby destabilizing strategic balances. Although regulatory bodies and international organizations have issued cybersecurity guidelines for nuclear facilities, implementation remains inconsistent. To address these challenges, the study proposes a set of resilience measures encompassing advanced technical safeguards, specialized workforce training, the establishment of international norms, and enhanced crisis communication protocols. Strengthening the cyber resilience of both civilian and military nuclear assets is presented as an urgent imperative for maintaining global security and strategic stability in the digital age.

This study introduces a new probability model for the risk priority number (RPN) in Failure Mode and Effects Analysis (FMEA), addressing limitations of the traditional RPN calculation, which assumes independence among severity, occurrence, and detection scores. Leveraging sufficient statistics within a Bayesian framework, the proposed model captures the inherent dependencies among these components, providing a more realistic and flexible representation of risk. Simulation studies validate the estimator's superior accuracy and stability, while empirical analyses on both AI risk assessment and gas refinery fire risk data sets demonstrate its effectiveness and adaptability across diverse domains and sampling strategies. Model comparisons using p-values and the Akaike information criterion (AIC) confirm the new model as the best fit for categorical risk data, aligning naturally with our theoretical approach. The results suggest that this new model enhances the reliability and interpretability of FMEA risk assessments, providing a powerful tool for decision making and risk mitigation in complex safety-critical systems.

Most nations across the globe have already embraced climate legislation to tackle the challenge of climate change. This article considers the role of country risk (i.e., economic risk, financial risk, political risk, and climate physical risk) in affecting the relationship between climate mitigation legislation (CML) on climate mitigation innovations (CMIs) using a panel of 130 countries from 1995 to 2022. The findings show that CML generally promotes CMI. However, moderating effects reveal that country risk can weaken the positive impacts of CML on CMI, underscoring the importance of integrating risk management into legislative frameworks to drive CMI. Asymmetry checks show that the direct and moderating effects are more pronounced in countries with greater CMI, suggesting that greater CMI requires stronger risk mitigation. Heterogeneity analysis reveals the moderating effect of risks on the impact of CML on CMI differs significantly between developed and developing countries, with developing countries facing a more urgent need for climate risk management.

Current assessments of port resilience primarily focus on the risks affecting its operations, often neglecting the ripple effects across different subsystems within a port. In multimodal container ports, these sub-systems include liner shipping, feeder shipping, railways, and trucking. Moreover, prevailing research predominantly addresses port resilience from a macro perspective without detailing micro-level operational concerns. This article proposes a new integrated methodology that not only considers but also quantifies the ripple effects across different multimodal sub-systems and their impact on overall port resilience. It employs real operational and accident data to assess the resilience of a multimodal container port under different disruption scenarios, hence providing valuable insights into preventing systemic failures through targeted interventions at the subsystem level. The proposed methodology comprises three principal components: a system dynamics (SD) simulation that integrates variables and factors affecting port resilience, a resilience analysis model that converts system performance into a resilience metric based on three fundamental criteria, and a comprehensive port system resilience assessment utilizing Evidential Reasoning (ER). Each step, from the detailed simulation model reflecting micro-level mechanisms to aggregating information across subsystems, builds toward determining the port's overall resilience. Multiple disruptive scenarios are designed and derived from historical failures and field investigations to validate the effectiveness of the proposed methodology. The results demonstrate that the proposed approach effectively assesses port performance under disruptions, identifies critical subsystems, and supports timely recovery strategies. Applicable to other port systems, this approach offers essential insights for improving long-term resilience in container port operations.

Workforce reductions, such as those implemented by the Department of Government Efficiency, can have far-reaching effects that extend beyond immediate job losses. This study employs a systems-based modeling approach, combining traditional Input-Output (IO) analysis with the inoperability Input-Output Model (IIM), to investigate how staffing cuts impact economic activity and erode institutional functions across interconnected sectors. The study reveals that reductions in federal staff have a significant impact on industries that rely heavily on government contracts and infrastructure, including aerospace, transportation, and high-tech services. These disruptions create ripple effects throughout supply networks and regional economies, resulting in delays, cancellations, and reduced operational capacity. Notably, the extent and pattern of losses identified here align with findings from independent reports, which highlight hidden costs such as declines in productivity, contract terminations, and maintenance backlogs that often offset the expected savings from workforce reductions. Unlike models that only focus on output, the IIM framework captures functional degradation, providing a more accurate breakdown of impacts on various economic sectors. These findings underscore the limitations of cost-cutting measures that overlook systemic interdependencies, highlighting the need for policies that strike a balance between fiscal objectives and institutional resilience. An adaptive, risk-aware approach to workforce planning can help maintain essential services while managing organizational change.

Modern slavery has become recognized as one of the world's great human rights challenges due to the high prevalence of coercive labor exploitation associated with the production and consumption of many goods and services across the globe. Yet, while its practice is commonly considered to be "unseen" and far removed from many people's everyday lives and working experiences, the micro-level bases of individual perceptions and actions taken in response to this "distal" threat remain poorly understood. In this paper, we develop and test a model linking the "psychological distance of modern slavery risk" to individual concerns, ethical organizational climate, and intentions to engage in mitigating behaviors in the workplace. Results from a survey of 511 working adults from UK businesses show that "closer" psychological distance to modern slavery is associated with higher levels of concern and greater intention to act in response to this risk. We also find that ethical climate moderates the impact of modern slavery risk concerns on intentions to engage in mitigating behaviors. Our study findings, therefore, complement existing research by pinpointing the key roles of psychological distance and ethical climate in modern slavery risk responses and highlighting the potential for micro-level interventions to help promote antislavery action.