Risk analysis : an official publication of the Society for Risk Analysis最新文献

{"title":"Systemic Risk: The Threat to Societal Diversity and Coherence.","authors":"Ortwin Renn, Klaus Lucas","doi":"10.1111/risa.13654","DOIUrl":"https://doi.org/10.1111/risa.13654","url":null,"abstract":"Insights from complexity science can be applied to the analysis of social processes in heterogeneous societies. Many features that characterize and influence complex structures in nearly every domain of nature, technology, and society can be derived from simple modeling processes in physics and chemistry. If one applies these features to the structure of social risks, a number of insights are gained that can be subject to further empirical analysis. In particular, they add—to the well‐known steering mechanisms of hierarchy, competition, and cooperation—the contribution of self‐organization, the effect of which is underestimated in almost all theories of social science. But in view of the crises facing modern democracy, such as migration and populism, it is precisely this mechanism of dynamic structure generation that is decisive for an effective and fair risk governance. In this article, we analyze the threat to societal diversity and coherence on the basis of complexity science.","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1921-1934"},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/risa.13654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38781857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID-19.","authors":"Hooman Parhizkar,&nbsp;Kevin G Van Den Wymelenberg,&nbsp;Charles N Haas,&nbsp;Richard L Corsi","doi":"10.1111/risa.13844","DOIUrl":"https://doi.org/10.1111/risa.13844","url":null,"abstract":"<p><p>Aerosol transmission has played a significant role in the transmission of COVID-19 disease worldwide. We developed a COVID-19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS-CoV-2 virus. The model calculates the concentration of size-resolved, virus-laden aerosol particles in well-mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single-zone filtration. The novelty of this model relates to the concept of \"inhaled & deposited dose\" in the respiratory system of receptors linked to a dose-response curve for human coronavirus HCoV-229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5-4 μm range expressed in picoliters (pL) in a well-documented COVID-19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose-response curve of HCoV-229E which provides a preliminary estimate of the average SARS-CoV-2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID-19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"2075-2088"},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662138/pdf/RISA-9999-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39573539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disaster Risk Planning With Fuzzy Goal Programming.","authors":"Terry R Rakes,&nbsp;Jason K Deane,&nbsp;Loren P Rees,&nbsp;David M Goldberg","doi":"10.1111/risa.13849","DOIUrl":"https://doi.org/10.1111/risa.13849","url":null,"abstract":"<p><p>The uncertainty in the timing and severity of disaster events makes the long-term planning of mitigation and recovery actions both critical and extremely difficult. Planners often use expected values for hazard occurrences, leaving communities vulnerable to worse-than-usual and even so-called \"black swan\" events. This research models disasters in terms of their best-case, most-likely, and worst-case damage estimates. These values are then embedded in a fuzzy goal programming model to provide community planners and stakeholders with the ability to strategize for any range of events from best-case to worst-case by adjusting goal weights. Examples are given illustrating the modeling approach, and an analysis is provided to illustrate how planners might use the model as a planning tool.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"2026-2040"},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39699950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From the editors.","authors":"Tony Cox,&nbsp;Karen Lowrie","doi":"10.1111/risa.14040","DOIUrl":"https://doi.org/10.1111/risa.14040","url":null,"abstract":"","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1893-1894"},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33519446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk.","authors":"Carl Macrae","doi":"10.1111/risa.13850","DOIUrl":"https://doi.org/10.1111/risa.13850","url":null,"abstract":"<p><p>Efforts to develop autonomous and intelligent systems (AIS) have exploded across a range of settings in recent years, from self-driving cars to medical diagnostic chatbots. These have the potential to bring enormous benefits to society but also have the potential to introduce new-or amplify existing-risks. As these emerging technologies become more widespread, one of the most critical risk management challenges is to ensure that failures of AIS can be rigorously analyzed and understood so that the safety of these systems can be effectively governed and improved. AIS are necessarily developed and deployed within complex human, social, and organizational systems, but to date there has been little systematic examination of the sociotechnical sources of risk and failure in AIS. Accordingly, this article develops a conceptual framework that characterizes key sociotechnical sources of risk in AIS by reanalyzing one of the most publicly reported failures to date: the 2018 fatal crash of Uber's self-driving car. Publicly available investigative reports were systematically analyzed using constant comparative analysis to identify key sources and patterns of sociotechnical risk. Five fundamental domains of sociotechnical risk were conceptualized-structural, organizational, technological, epistemic, and cultural-each indicated by particular patterns of sociotechnical failure. The resulting SOTEC framework of sociotechnical risk in AIS extends existing theories of risk in complex systems and highlights important practical and theoretical implications for managing risk and developing infrastructures of learning in AIS.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1999-2025"},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39652180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of Farmers' Vegetable Safety Production Based on Identification of Key Risk Factors From Beijing, China.","authors":"Zhengqing Yin,&nbsp;Bo Li,&nbsp;Dongyue Gu,&nbsp;Jian Huang,&nbsp;Lingxian Zhang","doi":"10.1111/risa.13843","DOIUrl":"https://doi.org/10.1111/risa.13843","url":null,"abstract":"<p><p>Food safety emphasizes risk control in the production process, and has attracted much attention from food regulators and consumers in recent years. The objectives of this study were to conduct early key risk factors identification and risk modeling for vegetable safety production. To achieve these objectives, this article quantitatively identified the key direct and indirect risk factors in vegetable safety production through questionnaire surveys and a multivariate linear model, and modeled the effects of key risk factors affecting vegetable safety production based on the catastrophe progression method. Based on 973 valid farmers' questionnaires from Beijing, China, the results showed that key direct risk factors are production violation, farmland biological control, pesticide and fertilizer use criteria, and agricultural consumable handling; key indirect risk factors included cooperative participation, planting years, prohibited pesticide knowledge, production recording, and product type. Through the empirical analysis, it can be seen that there are regional differences in the production risk of vegetable farmers in Beijing. The production risks of Changping, Huairou, and Shunyi are the most serious; from a city-wide perspective, the risk of farmland biological control is greatest, followed by risk aversion ability. The findings of this research have important implications for safe vegetable production and farmers' production risk control.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"2089-2106"},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39561789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning and other information analyses for risk in social networks.","authors":"Jun Zhuang,&nbsp;Chen Wang,&nbsp;James H Lambert","doi":"10.1111/risa.13945","DOIUrl":"https://doi.org/10.1111/risa.13945","url":null,"abstract":"The world has entered a digital era where people and machines leave digital footprints in websites, social media, cameras, sensor logs, and mobile devices. For example, manufacturing and operating systems collect streaming data through sensors and the Internet of Things (IoT); vehicles generate vast amounts of trajectory and sensor log data in aviation and surface transportation. Whether from natural, technological, or adversarial hazards, risks arise from various root causes, including human errors. Risk analysis has opportunities to integrate big data, natural language processing, computer vision, and machine learning methods in the digital era. This special issue features papers presented at the Conference on Risk Analysis, Decision Analysis and Security, Buffalo/Niagara Falls, NY, July 30–August 2, 2019 (organized by Drs. Jun Zhuang and Chen Wang). Collectively, the papers describe cutting-edge research on the possibilities of harnessing high-volume, high-dimensional, multisource, and multimodal data to give insights for risk assessment, communication, and management, as well as cover perspectives discussing the scope and limitations of big data risk analytics. The applications include food safety, cyber security, disaster mitigation, and recovery, misinformation and disinformation in social media, aviation safety, insurance fraud detection, health risk for emergency responders, autonomous driving, privacy risk management, and service failure prediction in transportation. The data utilized by these studies range from streaming data (e.g., from online social media, flight data recorders, and sensors of operating equipment), event data (e.g., fraud records of insurance, logs of stress leaves of emergency responders, and food safety incidents), to expert judgments. The methodologies cover a broad spectrum of analytic modeling, statistical inference, machine learning, expert elicitation, and combinations. Welburn and Strong propose an analytic framework to describe the systemic cyber risk resulting from cascading common cause or independent failures following a cyber incident. They apply the sector-level input–output analysis in economics to assess the aggregate losses associated with firm-level cyber incidents. Their model is validated using a cyber-attack case with known damages. The model can help determine cyber insurance premiums and make cybersecurity policies. Allodi et al. propose a model of a “work averse” attacker in a cybersecurity setting where the attacker is inclined to adopt existing toolkits if they can cause enough harm to the systems rather than develop exploits for new vulnerabilities. The authors build hypotheses based on this model, and evaluate these hypotheses using a large-scale dataset involving two million attack signatures recorded by Symantec against an extensive collection of information systems. The analytic and empirical analyses provide an example of work-aversion tendencies of cyber attackers using mass (but imper","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1603-1605"},"PeriodicalIF":3.8,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40605542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems.","authors":"Abbas Mamudu,&nbsp;Faisal Khan,&nbsp;Sohrab Zendehboudi,&nbsp;Sunday Adedigba","doi":"10.1111/risa.13829","DOIUrl":"https://doi.org/10.1111/risa.13829","url":null,"abstract":"<p><p>This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic risks-based results produced from an adaptive loss function model for reservoir production losses/dynamic economic risks assessments. The methodology employs a multilayer-perceptron (MLP) model, a loss function model; it integrates an early warning index system (EWIS) of oilfield block with a BN model for process modeling. The model evaluates the evidence-based economic consequences of the production losses and analyzes the statistical disparities of production predictions using an EWIS-assisted BN model and the loss function model at the same time. The proposed methodology introduces an innovative approach that effectively minimizes the potential for dynamic economic risks. The model predicts real-time daily production/dynamic economic losses. The connectionist model yields an encouraging overall predictive performance with average errors of 1.954% and 1.957% for the two case studies: cases 1 and 2, respectively. The model can determine transitional/threshold production values for adequate reservoir management toward minimal losses. The results show minimum average daily dynamic economic losses of $267,463 and $146,770 for cases 1 and 2, respectively. It is a multipurpose tool that can be recommended for the field operators in petroleum reservoir production management related decision making.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1541-1570"},"PeriodicalIF":3.8,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39717701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is Crisis and Emergency Risk Communication as Effective as Vaccination for Preventing Virus Diffusion? Measuring the Impacts of Failure in CERC with MERS-CoV Outbreak in South Korea.","authors":"Ho Young Yoon","doi":"10.1111/risa.13842","DOIUrl":"https://doi.org/10.1111/risa.13842","url":null,"abstract":"<p><p>This study measured the impacts of failure in Crisis and Emergency Risk Communication (CERC) during the outbreak of a contagious Corona viral disease. The study measured the impacts by the number of individuals and hospitals exposed to the virus. The 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea was used to investigate the consequences of CERC failure, where the names of hospitals exposed to MERS-CoV were withheld from the public during the early stage of virus diffusion. Empirical data analyses and simulated model tests were conducted. The findings of analyses and tests show that an early announcement of the hospital names and publicizing the necessary preventive measures could have reduced the rate of infection by approximately 85% and the number of contaminated healthcare facilities by 39% at maximum. This level of reduction is comparable to that of vaccination and of social distancing.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1504-1523"},"PeriodicalIF":3.8,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8661923/pdf/RISA-42-1504.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39547415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk Amplification and Attenuation as Communication Strategies in Climate Adaptation in Urban Areas.","authors":"Kirstin Dow,&nbsp;Seth Tuler","doi":"10.1111/risa.13819","DOIUrl":"https://doi.org/10.1111/risa.13819","url":null,"abstract":"<p><p>Climate risks are motivating adaptation with local municipal actors becoming key participants in a complex web of climate risk communication. Some cities have created civil service positions focused on climate resilience. We conducted interviews with six such individuals in four U.S. Atlantic coast cities to investigate how they think about and negotiate communication challenges associated with implementation of climate resilience strategies. We grounded our study in the Social Amplification of Risk Framework (SARF), which despite its longevity and wide usage has rarely been used to understand the role of government actors. We found substantial complexity in how these government representatives develop both amplifying and attenuating communication strategies as they often simultaneously reach multiple audiences holding different perspectives. They are familiar with and employ risk communication practices. However, they report needing to modify their efforts as climate adaptation issues and goals evolve over time, and experiment in situations, such as discussions of retreat, where established communication practices provide insufficient guidance. In order to develop a deeper understanding of the governmental risk communication actors, we suggest four potential avenues for taking advantage of the strengths of SARF as a framework for connecting and integrating with other models and theories. We also propose several directions for research based on the challenges these practitioners are finding in their work to facilitate adaptation to climate risks. The activity of government actors is rich in its applied risk communication practice and its challenges offer new questions to expand our thinking about the SARF and risk communication more broadly.</p>","PeriodicalId":517072,"journal":{"name":"Risk analysis : an official publication of the Society for Risk Analysis","volume":" ","pages":"1440-1454"},"PeriodicalIF":3.8,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39467219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}