Risk Analysis: An International Journal
The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of experts’ opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.
A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods
Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system‐wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.
Critical Time, Space, and Decision‐Making Agent Considerations in Human‐Centered Interdisciplinary Hurricane‐Related Research
In hazard and disaster contexts, human‐centered approaches are promising for interdisciplinary research since humans and communities feature prominently in many definitions of disaster and the built environment is designed and constructed by humans to serve their needs. With a human‐centered approach, the decision‐making agent becomes a critical consideration. This article discusses and illustrates the need for alignment of decision‐making agents, time, and space for interdisciplinary research on hurricanes, particularly evacuation and the immediate aftermath. We specifically consider the fields of sociobehavioral science, transportation engineering, power systems engineering, and decision support systems in this context. These disciplines have historically adopted different decision‐making agents, ranging from individuals to households to utilities and government agencies. The fields largely converged to the local level for studies’ spatial scales, with some extensions based on the physical construction and operation of some systems. Greater discrepancy across the fields is found in the frequency of data collection, which ranges from one time (e.g., surveys) to continuous monitoring systems (e.g., sensors). Resolving these differences is important for the success of interdisciplinary teams in protective‐action‐related disaster research.
Quantitative Risk Assessment of Seafarers’ Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling
Reducing the incidence of seafarers’ workplace injuries is of great importance to shipping and ship management companies. The objective of this study is to identify the important influencing factors and to build a quantitative model for the injury risk analysis aboard ships, so as to provide a decision support framework for effective injury prevention and management. Most of the previous research on seafarers’ occupational accidents either adopts a qualitative approach or applies simple descriptive statistics for analyses. In this study, the advanced method of a Bayesian network (BN) is used for the predictive modeling of seafarer injuries for its interpretative power as well as predictive capacity. The modeling is data driven and based on an extensive empirical survey to collect data on seafarers’ working practice and their injury records during the latest tour of duty, which could overcome the limitation of historical injury databases that mostly contain only data about the injured group instead of the entire population. Using the survey data, a BN model was developed consisting of nine major variables, including “PPE availability,” “Age,” and “Experience” of the seafarers, which were identified to be the most influential risk factors. The model was validated further with several tests through sensitivity analyses and logical axiom test. Finally, implementation of the result toward decision support for safety management in the global shipping industry was discussed.
Mediating and Moderating Roles of Trust in Government in Effective Risk Rumor Management: A Test Case of Radiation‐Contaminated Seafood in South Korea
This study has two aims: to identify effective strategies for managing false rumors about risks and to investigate the roles that basic and situational trust in government play in that process. Online experiment data were collected nationwide from 915 adults in South Korea. They were exposed to a false rumor about radiation‐contaminated seafood and were randomly assigned to one of three rumor response conditions (refutation, denial, attack the attacker). One‐way ANOVA indicated that the refutation response yielded the highest level of situational trust in government response (TGR). Results of moderated mediation models using the PROCESS Macro indicated the following. (1) The refutation response had a positive effect on TGR, and the attack response had a negative effect. (2) There were significant interaction effects between the attack response and preexisting basic trust in government (BTG) in that the attack response had a negative effect on TGR only when BTG was low. (3) TGR significantly mediated the relationship between each of the three rumor responses and two dependent variables (intentions for rumor dissemination and for unwarranted actions), but in dramatically different ways across the responses. This study provides evidence for the superior effectiveness of the refutation rumor response and identifies specific roles of trust in government in the risk rumor management process.
Unmanned aircrafts (UA) usually fly below 500 ft to be segregated from manned aircraft. However, while general aviation (GA) usually do fly above 500 ft in areas where UA are allowed to operate, GA will at times also fly below 500 ft. Consequently, there is a distinct risk of near‐miss encounters as well as actual midair collisions (MACs). This work presents a model for determining this risk based on physical parameters of the aircraft and actual figures for the numbers of GA in a given airspace, as well as the probability of having GA below 500 ft. The aim is to achieve a prediction with a precision better than one order of magnitude relative to the true MAC rate value. The model is applied to Danish airspace and the MAC rate for unmitigated operations of UA is found to be approximately 10−6 MAC per flight hour. The model is particularly well suited for beyond visual line‐of‐sight operations, and is useful for UA operators for conducting risk assessment of planned operations as well as for regulators for determining appropriate operational requirements.
Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda
The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators—accuracy, precision, and the root mean square error (RMSE)—were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central‐east Africa.
Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages
The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.
Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission
Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.
This mixed‐methods study investigated consumers’ knowledge of chemicals in terms of basic principles of toxicology and then related this knowledge, in addition to other factors, to their fear of chemical substances (i.e., chemophobia). Both qualitative interviews and a large‐scale online survey were conducted in the German‐speaking part of Switzerland. A Mokken scale was developed to measure laypeople's toxicological knowledge. The results indicate that most laypeople are unaware of the similarities between natural and synthetic chemicals in terms of certain toxicological principles. Furthermore, their associations with the term “chemical substances” and the self‐reported affect prompted by these associations are mostly negative. The results also suggest that knowledge of basic principles of toxicology, self‐reported affect evoked by the term “chemical substances,” risk‐benefit perceptions concerning synthetic chemicals, and trust in regulation processes are all negatively associated with chemophobia, while general health concerns are positively related to chemophobia. Thus, to enhance informed consumer decisionmaking, it might be necessary to tackle the stigmatization of the term “chemical substances” as well as address and clarify prevalent misconceptions.
Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system‐wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.
Many real‐world systems use mission aborts to enhance their survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a risk of a system loss in the case of a mission continuation becomes too high. Usually, the rescue or recovery procedure is initiated upon the mission abort. Previous works have discussed a setting when only one attempt to complete a mission is allowed and this attempt can be aborted. However, missions with a possibility of multiple attempts can occur in different real‐world settings when accomplishing a mission is really important and the cost‐related and the time‐wise restrictions for this are not very severe. The probabilistic model for the multiattempt case is suggested and the tradeoff between the overall mission success probability (MSP) and a system loss probability is discussed. The corresponding optimization problems are formulated. For the considered illustrative example, a detailed sensitivity analysis is performed that shows specifically that even when the system's survival is not so important, mission aborting can be used to maximize the multiattempt MSP.
Mega‐Review: Causality Books Causal Analytics for Applied Risk Analysis by Louis Anthony Cox, Jr., Douglas A. Popken, and Richard X. Sun. Springer, International Series in Operations Research & Management Science, Vol. 270, 2018, $229, xxii+588. The...
Risk and Planning in Agriculture: How Planning on Dairy Farms in Ireland Is Affected by Farmers’ Regulatory Focus
This article examines how planning on dairy farms is affected by farmers' motivation. It argues that farmers' choice of expansion strategies can be specified in terms of risk decision making and understood as either prevention‐focused or promotion‐focused motivation. This relationship was empirically examined using mediated regression analyses where promotion/prevention focus was the independent variable and its effect on total milk production via planned expansion strategies was examined. The results indicate that promotion focus among farmers has an indirect effect on farm expansion via planning strategies that incur greater risk to the farm enterprise. Regulatory focus on the part of farmers has an influence on farmers' planning and risk management activities and must be accounted for in the design and implementation of policy and risk management tools in agriculture.
Clinical Capital and the Risk of Maternal Labor and Delivery Complications: Hospital Scheduling, Timing, and Cohort Turnover Effects
The establishment of interventions to maximize maternal health requires the identification of modifiable risk factors. Toward the identification of modifiable hospital‐based factors, we analyze over 2 million births from 2005 to 2010 in Texas, employing a series of quasi‐experimental tests involving hourly, daily, and monthly circumstances where medical service quality (or clinical capital) is known to vary exogenously. Motivated by a clinician's choice model, we investigate whether maternal delivery complications (1) vary by work shift, (2) increase by the hours worked within shifts, (3) increase on weekends and holidays when hospitals are typically understaffed, and (4) are higher in July when a new cohort of residents enter teaching hospitals. We find consistent evidence of a sizable statistical relationship between deliveries during nonstandard schedules and negative patient outcomes. Delivery complications are higher during night shifts (OR = 1.21, 95% CI: 1.18–1.25), and on weekends (OR = 1.09, 95% CI: 1.04–1.14) and holidays (OR = 1.29, 95% CI: 1.04–1.60), when hospitals are understaffed and less experienced doctors are more likely to work. Within shifts, we show deterioration of occupational performance per additional hour worked (OR = 1.02, 95% CI: 1.01–1.02). We observe substantial additional risk at teaching hospitals in July (OR = 1.28, 95% CI: 1.14–1.43), reflecting a cohort‐turnover effect. All results are robust to the exclusion of noninduced births and intuitively falsified with analyses of chromosomal disorders. Results from our multiple‐test strategy indicate that hospitals can meaningfully attenuate harm to maternal health through strategic scheduling of staff.
In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.
Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent‐based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent‐based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent‐based modeling and Monte Carlo simulation at its core. Agent‐based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks.
The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.
When Evolution Works Against the Future: Disgust's Contributions to the Acceptance of New Food Technologies
New food technologies have a high potential to transform the current resource‐consuming food system to a more efficient and sustainable one, but public acceptance of new food technologies is rather low. Such an avoidance might be maintained by a deeply preserved risk avoidance system called disgust. In an online survey, participants (N = 313) received information about a variety of new food technology applications (i.e., genetically modified meat/fish, edible nanotechnology coating film, nanotechnology food box, artificial meat/milk, and a synthetic food additive). Every new food technology application was rated according to the respondent's willingness to eat (WTE) it (i.e., acceptance), risk, benefit, and disgust perceptions. Furthermore, food disgust sensitivity was measured using the Food Disgust Scale. Overall, the WTE both gene‐technology applications and meat coated with an edible nanotechnology film were low and disgust responses toward all three applications were high. In full mediation models, food disgust sensitivity predicted the disgust response toward each new food technology application, which in turn influenced WTE them. Effects of disgust responses on the WTE a synthetic food additive were highest for and lowest for the edible nanotechnology coating film compared to the other technologies. Results indicate that direct disgust responses influence acceptance and risk and benefit perceptions of new food technologies. Beyond the discussion of this study, implications for future research and strategies to increase acceptance of new food technologies are discussed.
Public and private actors with critical roles for ensuring societal safety need to work proactively to reduce risks and vulnerabilities. Traditionally, risk management activities have often been performed in order to ensure continuous functioning of key societal services. Recently, however, business continuity management (BCM), and its analytical subcomponent business impact assessment (BIA), has been introduced and used more extensively by both the private and public sector in order to increase the robustness and resilience of critical infrastructures and societal functions and services. BCM was originally developed in the business sector but has received a broader use during the last decade. Yet, BCM/BIA has gained limited attention in the scientific literature—especially when it comes to clarifying and developing its conceptual basis. First, this article examines and discusses the conceptual foundation of BCM concepts, including practical challenges of applying the concepts. Based on recent conceptual developments from the field of risk management, a developed conceptualization is suggested. Second, the article discusses challenges that arise when applying BCM in the societal safety area and provides some recommendations aiming to improve the clarity and quality of applications. Third, the article provides suggestions of how to integrate the overlapping approaches of BIA and risk assessment in order to improve efficiency and effectiveness of proactive, analytic processes. We hope that the article can stimulate a critical discussion about the key concepts of BCM, their wider use in societal safety, and their connection to other concepts and activities such as risk assessment.
“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.