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��ࡱ�>�� ������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������u �r�"ibjbj�n�n2���a��aa �������""������������8�le��pl��������yp[p[p[p[p[p[p$fr��tfp������p����4�peee������ype�ypeee�������i,3������w@eep�p0�pebu�fbuee�0bu�-m��e�����pp�h����p������������������������������������������������������������������������bu���������"q s: risk analysis volume 42, issue 8, august 2022 1. title: systemic cyber risk and aggregate impacts authors: jonathan w. welburn, aaron m. strong abstract: with some of the largest cyber attacks occurring in recent years�from 2010 to 2019�we are only beginning to understand the full extent of cyber risk. as businesses grapple with the risks of cyber-incidents and their imperfect ability to prevent them, attention has shifted toward risk management and insurance. while there have been efforts to understand the costs of cyber attacks, the systemic risk�a result of risks spreading across interdependent systems�associated with cyber attacks remains a critical and problem in need of further study. we contribute a theoretical framework that describes systemic cyber risk as the result of cascading, common cause, or independent failures following a cyber incident. we construct a quantitative model of cascading failures to estimate the potential economic damage associated with a given cyber incident. we present an interdisciplinary approach for extending standard sector-level input�output analyses to the cyber domain, which has not been done. we estimate the aggregate losses associated with firm-level incidents, a contribution to risk analysis and computational economic modeling. we use this model to estimate the impact of potential cyber incidents and compare model results to a case with known damages. finally, we use the model of systemic cyber failure to consider the implications on the growing cyber insurance market and the need for broader cyber policy. while we discuss the topic of systemic cyber risk, our contribution of using i/o analysis to estimate the aggregate losses from firm-level incidents is applicable across a variety of risk analysis applications from environment to health. 2. title: the work-averse cyberattacker model: theory and evidence from two million attack signatures authors: luca allodi, fabio massacci, julian williams abstract: the assumption that a cyberattacker will potentially exploit all present vulnerabilities drives most modern cyber risk management practices and the corresponding security investments. we propose a new attacker model, based on dynamic optimization, where we demonstrate that large, initial, fixed costs of exploit development induce attackers to delay implementation and deployment of exploits of vulnerabilities. the theoretical model predicts that mass attackers will preferably (i) exploit only one vulnerability per software version, (ii) largely include only vulnerabilities requiring low attack complexity, and (iii) be slow at trying to weaponize new vulnerabilities . these predictions are empirically validated on a large data set of observed massed attacks launched against a large collection of information systems. findings in this article allow cyber risk managers to better concentrate their efforts for vulnerability management, and set a new theoretical and empirical basis for further research defining attacker (offensive) processes. 3. title: defining cyber security and cyber security risk within a multidisciplinary context using expert elicitation authors: mariana g. cains, liberty flora, danica taber, zoe king, diane s. henshel abstract: it is important to have and use standardized terminology and develop a comprehensive common understanding of what is meant by cyber security and cyber security risk given the multidisciplinary nature of cyber security and the pervasiveness of cyber security concerns throughout society. using expert elicitation methods, collaborating cyber researchers from multiple disciplines and two sectors (academia, government�military) were individually interviewed and asked to define cyber security and cyber security risk. data-driven thematic analysis was used to identify the most salient themes within each definition, sector, and cyber expert group as a whole with results compared to current standards definitions. network analysis was employed to visualize the interconnection of salient themes within and across sectors and disciplines. when examined as a whole group, �context-driven,� �resilient system functionality,� and �maintenance of cia (confidentiality, integrity, availability)� were the most salient themes and influential network nodes for the definition of cyber security, while �impacts of cia vulnerabilities,� �probabilities of outcomes,� and �context-driven� were the most salient themes for cyber security risk. we used this expert elicitation process to develop comprehensive definitions of cyber security (cybersecurity) and cyber security risk that encompass the contextual frameworks of all the disciplines represented in the collaboration and explicitly incorporates human factors as significant cyber security risk factors. 4. title: disaster recovery communication in the digital era: social media and the 2016 southern louisiana flood authors: jungwon yeo, claire connolly knox, qian hu abstract: this study explores disaster recovery communication in the digital era. in particular, this study analyzes twitter communication data corresponding to the 2016 southern louisiana flood recovery process and examines patterns and characteristics of long-term recovery communication. based on network and sentiment analyses of the longitudinal twitter data, the study identifies the dynamic changes in participants� numbers, dominant voices, and sentiments in social media communication during the long-term recovery process. from the additional content analysis of relevant news articles, in-depth contextual information is provided to support and supplement the findings. findings show the weaning communication volume during the recovery phase, lacking local voices over the long-term recovery communication process, and prolonging negative sentiments over the recovery period. based on the findings, the authors provide implications highlighting the need for investing in long-term recovery communication, better utilizing information from social media, and supporting local voices during disaster recovery. 5. title: social cohesion: mitigating societal risk in case studies of digital media in hurricanes harvey, irma, and maria authors: gabriela gongora-svartzman, jose e. ramirez-marquez abstract: natural disasters affect thousands of communities every year, leaving behind human losses, billions of dollars in rebuilding efforts, and psychological affectation in survivors. how fast a community recovers from a disaster or even how well a community can mitigate risk from disasters depends on how resilient that community is. one main factor that influences communities' resilience is how a community comes together in times of need. social cohesion is considered to be�the glue that holds society together, which can be better examined in a critical situation. there is no consensus on measuring social cohesion, but recent literature indicates that social media communications and communities play an essential role in today's disaster mitigation strategies.this research explores how to quantify social cohesion through social media outlets during disasters. the approach involves combining and implementing text processing techniques and graph network analysis to understand the relationships between nine different types of participants during hurricanes harvey, irma, and maria. visualizations are employed to illustrate these connections, their evolution before, during, and after disasters, and the degree of social cohesion throughout their timeline. the proposed measurement of social cohesion through social media networks presented in this work can provide future risk management and disaster mitigation policies. this social cohesion measure identifies the types of actors in a social network and how this network varies daily. therefore, decisionmakers could use this measure to release strategic communication before, during, and after a disaster strikes, thus providing relevant information to people in need. 6. title: lies, damned lies, and social media following extreme events authors: katie byrd, richard s. john abstract: with the increased use of social media in crisis communication following extreme events, it is important to understand how the public distinguishes between true and false information. a u.s. adult sample (n = 588) was presented 20 actual social media posts following a natural disaster or soft-target terrorist attack in the united states. in this study, social media posts are conceptualized as truth signals with varying strengths, either above or below each individual's threshold for believing the post is true. optimally, thresholds should be contingent on the (incentivized) error penalties and base-rate of true posts, both of which were manipulated. separate receiver operating characteristic (roc) analyses indicate that participants performed slightly better than chance for natural disasters and moderately better than chance for terror attacks. while the pooled thresholds are ordinally consistent with the base-rate and error penalty manipulations, they are underadjusted compared to the optimal thresholds. after accounting for demographic and cognitive variables, the base-rate manipulation significantly predicted sensitivity, specificity, and true response rates in the expected direction for both content domains, while the error penalty manipulation had no significant effect in either domain. self-identified political conservatives performed worse at classifying false content as false for natural disasters, but better for terror attacks. 7. title: monitoring misinformation on twitter during crisis events: a machine learning approach authors: kyle hunt, puneet agarwal, jun zhuang abstract: social media has been increasingly utilized to spread breaking news and risk communications during disasters of all magnitudes. unfortunately, due to the unmoderated nature of social media platforms such as twitter, rumors and misinformation are able to propagate widely. given this, a surfeit of research has studied false rumor diffusion on twitter, especially during natural disasters. within this domain, studies have also focused on the misinformation control efforts from government organizations and other major agencies. a prodigious gap in research exists in studying the monitoring of misinformation on social media platforms in times of disasters and other crisis events. such studies would offer organizations and agencies new tools and ideologies to monitor misinformation on platforms such as twitter, and make informed decisions on whether or not to use their resources in order to debunk. in this work, we fill the research gap by developing a machine learning framework to predict the veracity of tweets that are spread during crisis events. the tweets are tracked based on the veracity of their content as either true, false, or neutral. we conduct four separate studies, and the results suggest that our framework is capable of tracking multiple cases of misinformation simultaneously, with scores exceeding 87%. in the case of tracking a single case of misinformation, our framework reaches an score of 83%. we collect and drive the algorithms with 15,952 misinformation-related tweets from the boston marathon bombing (2013), manchester arena bombing (2017), hurricane harvey (2017), hurricane irma (2017), and the hawaii ballistic missile false alert (2018). this article provides novel insights on how to efficiently monitor misinformation that is spread during disasters. 8. title: text mining approaches for postmarket food safety surveillance using online media authors: david m. goldberg, samee khan, nohel zaman, richard j. gruss, alan s. abrahams abstract: food contamination and food poisoning pose enormous risks to consumers across the world. as discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. we compile a large data set of labeled consumer posts spanning two major websites. utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers� interactions with hazardous food products. we compare our methods to traditional sentiment-based text mining. we assess performance in a high-volume setting, utilizing a data set of over 4 million online reviews. our methods were 77�90% accurate in top-ranking reviews, while sentiment analysis was just 11�26% accurate. moreover, we aggregate review-level results to make product-level risk assessments. a panel of 21 food safety experts assessed our model's hazard-flagged products to exhibit substantially higher risk than baseline products. we suggest the use of these tools to profile food items and assess risk, building a postmarket decision support system to identify hazardous food products. our research contributes to the literature and practice by providing practical and inexpensive means for rapidly monitoring food safety in real time. 9. title: blame attribution asymmetry in human�automation cooperation authors: peng liu, yong du abstract: human�automation cooperation has become ubiquitous. in this concept, automation refers to autonomous machines, robots, artificial intelligence, and other autonomous nonhuman agents. a human driver will share control of semiautonomous vehicles (semi-avs) with an automated system and thus share responsibility for crashes caused by semi-avs. research has not clarified whether and why people would attribute different levels of blame and responsibility to automation (and its creators) and its human counterpart when each causes an equivalent crash. we conducted four experiments in two studies (total n = 1,045) to measure different responses (e.g., severity and acceptability judgment, blame and responsibility attribution, compensation judgment) to hypothetical crashes that are caused by the human or the automation in semi-avs. the results provided previously unidentified evidence of a bias, which we called the �blame attribution asymmetry,� a tendency that people will judge the automation-caused crash more harshly, ascribe more blame and responsibility to automation and its creators, and think the victim in this crash should be compensated more. this asymmetry arises in part because of the higher negative affect triggered by the automation-caused crash. this bias has a direct policy implication: a policy allowing �not-safe enough� semi-avs on roads could backfire, because these avs will lead to many traffic crashes, which might in turn produce greater psychological costs and deter more people from adopting them. other theoretical and policy implications of our findings were also discussed. 10. title: privacy accountability and penalties for iot firms authors: francesco ciardiello, andrea di liddo abstract: internet of things (iot) business partnership are formed by technological partners and traditional manufacturers. iot sensors and devices capture data from manufacturers' products. data enforce product/service innovation thanks to data sharing among companies. however, data sharing among firms increases the risk of data breaches. the latter is due to two phenomena: information linkage and privacy interdependency. data protection authorities (dpa) protect data users' rights and fine firms if there is an infringement of privacy laws. dpa sanction the responsible for the infringement of privacy laws. we present two different business scenarios: the first occurs when each firm is a data owner; the second occurs when only the manufacturer is the data owner. for both scenarios, we present two fair penalty schemes that suggest the following: total amount of the fine; and how to share the fine among participants. penalties critically vary at how innovation networks are structured in iot industries. our penalties provide incentives to data sharing since they redistribute firms' responsibility against data breaches. our penalties may mitigate the risk on the manufacturer if is the unique responsible for data handling. 11. title: when outcomes are not enough: an examination of abductive and deductive logical approaches to risk analysis in aviation authors: matthew stogsdill abstract: while airlines generate massive amounts of operational data every year, the ability to use the collected material to improve safety has begun to plateau. with the increasing demand for air travel, the aviation industry is continually growing while simultaneously being required to ensure the level of safety within the system remains constant. the purpose of this article is to explore whether the traditional analysis methods that have historically made aviation ultra-safe have reached their theoretical limits or merely practical ones. this analysis argues that the underlying logic governing the traditional (and current) approaches to assess safety and risk within aviation (and other safety critical systems) is abductive and therefore focused on creating explanations rather than predictions. while the current �fly-fix-fly� approach has, and will continue to be, instrumental in improving what (clearly) fails, alternative methods are needed to determine if a specific operation is more or less risky than others. as the system grows, so too does the number of ways it can fail, creating the possibility that more novel accidents may occur. the article concludes by proposing an alternative approach that explicitly adds temporality to the concepts of safety and risk. with this addition, a deductive analysis approach can be adopted which, while low in explanatory power, can be used to create predictions that are not bound to analyzing only outcomes that have occurred in the past but instead focuses on determining the deviation magnitude between the operation under analysis and historically commensurate operations. 12. title: development of a metric concept that differentiates between normal and abnormal operational aviation data authors: matthew stogsdill, daniele baranzini, pernilla ulfvengren abstract: there is a strong and growing interest in using the large amount of high-quality operational data available within an airline. one reason for this is the push by regulators to use data to demonstrate safety performance by monitoring the outputs of safety performance indicators relative to targeted goals. however, the current exceedance-based approaches alone do not provide sufficient operational risk information to support managers and operators making proximate real-time data-driven decisions. the purpose of this study was to develop and test a set of metrics which can complement the current exceedance-based methods. the approach was to develop two construct variables that were designed with the aim to: (1) create an aggregate construct variable that can differentiate between normal and abnormal landings (row_mean); and (2) determine if temporal sequence patterns can be detected within the data set that can differentiate between the two landing groups (row_sequence). to assess the differentiation ability of the aggregate constructs, a set of both statistical and visual tests were run in order to detect quantitative and qualitative differences between the data series representing two landing groups prior to touchdown. the result, verified with a time series k-means cluster analysis, show that the composite constructs seem to differentiate normal and abnormal landings by capturing time-varying importance of individual variables in the final 300 seconds before touchdown. together the approaches discussed in this article present an interesting and complementary way forward that should be further pursued. 13. title: quantifying the impact of environment factors on the risk of medical responders� stress-related absenteeism authors: mario p. brito, zhiyin chen, james wise, simon mortimore abstract: medical emergency response staff are exposed to incidents which may involve high-acuity patients or some intractable or traumatic situations. previous studies on emergency response staff stress-related absence have focused on perceived factors and their impacts on absence leave. to date, analytical models on absenteeism risk prediction use past absenteeism to predict risk of future absenteeism. we show that these approaches ignore environment data, such as stress factors. the increased use of digital systems in emergency services allows us to gather data that were not available in the past and to apply a data-driven approach to quantify the effect of environment variables on the risk of stress-related absenteeism. we propose a two-stage data-driven framework to identify the variables of importance and to quantify their impact on medical staff stress-related risk of absenteeism. first, machine learning techniques are applied to identify the importance of different stressors on staff stress-related risk of absenteeism. second, the cox proportional-hazards model is applied to estimate the relative risk of each stressor. four significant stressors are identified, these are the average night shift, past stress leave, the squared term of death confirmed by the emergency services and completion of the safeguarding form. we discuss counterintuitive results and implications to policy. 14. title: analyzing risk of service failures in heavy haul rail lines: a hybrid approach for imbalanced data authors: faeze ghofrani, hongyue sun, qing he abstract: an incident in which a rail defect of size over a threshold value is noticed and the track is taken out of service is known as a service failure. this article aims at building accurate prediction models with binary outcome for risk of service failures on heavy haul rail segments. an analysis of the factors that influence the risk of a service failure is conducted and quantitative models are developed to predict locations where service failures are most likely to occur until the next inspection. to this end, data are collected from a class i u.s. railroads for six years from 2011 to 2016. four prediction models (i.e., logistic regression, decision tree, multilayer perceptron, and gradient boosting classifier) are implemented and their results are compared. to account for the imbalanced classes between the normal operation and service failure, two treatments have been used including undersampling and oversampling. to improve the model performance, the parameters of each method are tuned using random search hyperparameter optimization. later, bootstrap aggregation (or bagging) is incorporated into each method. the findings of the study show that the prediction performance is the highest when using bagging and oversampling as treatments with gradient boosting method. it was also identified that gross tonnage, presence of geometry defects, ambient temperature, segment length, and rail defect presence are the most important factors for predicting the risk of service failures. the results of this study are useful for railroads to develop effective strategies for rail inspections, preventive maintenance, and capital planning. 15. title: social network analytics for supervised fraud detection in insurance authors: mar�a �skarsd�ttir, waqas ahmed, katrien antonio, bart baesens, r�mi dendievel, tom donas, tom reynkens abstract: insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. this contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. first, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. next, we establish fraud as a social phenomenon in the network and use the birank algorithm with a fraud-specific query vector to compute a fraud score for each claim. from the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable. although we build a model for only motor insurance, the network includes claims from all available lines of business. our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim-specific features. combining network and claim-specific features further improves the performance of supervised learning models to detect fraud. the resulting model flags highly suspicions claims that need to be further investigated. our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.       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