Risk Analysis: An International Journal
With the application of risk management and accident response in the railway domain, risk detection and prevention have become key research topics. Many dangers and associated risk sources must be considered in collaborative scenarios of heavy‐haul railways. In these scenarios, (1) various risk sources are involved in different data sources, and context affects their occurrence, (2) the relationships between contexts and risk sources in the accident cause mechanism need to be explicitly defined, and (3) risk knowledge reasoning needs to integrate knowledge from multiple data sources to achieve comprehensive results. To express the association rules among core concepts, this article constructs two ontologies: The accident‐risk ontology and the context ontology. Concept analysis is based on railway domain knowledge and accident analysis reports. To sustainably integrate knowledge, an integrated evolutionary model called scenario‐risk‐accident chain ontology (SRAC) is constructed by introducing new data sources. The SRAC is integrated through expert rules between the two ontologies, and its evolution process involves new knowledge through a new risk source database. After three versions of the upgrade process, potential risk sources can be mined and evaluated in specific contexts. To evaluate the risk source level, a long short‐term memory (LSTM) neural network model is used to capture context and risk text features. A model comparison for different neural network structures is performed to find the optimal evaluation results. Finally, new concepts, such as risk source level, and new instances are updated in the context‐aware risk knowledge reasoning framework.
Hydrometeorological phenomena have increased in intensity and frequency in last decades, with Europe as one of the most affected areas. This accounts for considerable economic losses in the region. Regional adaptation strategies for costs minimization require a comprehensive assessment of the disasters’ economic impacts at a multiple‐region scale. This article adapts the flood footprint method for multiple‐region assessment of total economic impact and applies it to the 2009 Central European Floods event. The flood footprint is an impact accounting framework based on the input–output methodology to economically assess the physical damage (direct) and production shortfalls (indirect) within a region and wider economic networks, caused by a climate disaster. Here, the model is extended through the capital matrix, to enable diverse recovery strategies. According to the results, indirect losses represent a considerable proportion of the total costs of a natural disaster, and most of them occur in nonhighly directly impacted industries. For the 2009 Central European Floods, the indirect losses represent 65% out of total, and 70% of it comes from four industries: business services, manufacture general, construction, and commerce. Additionally, results show that more industrialized economies would suffer more indirect losses than less‐industrialized ones, in spite of being less vulnerable to direct shocks. This may link to their specific economic structures of high capital‐intensity and strong interindustrial linkages.
Risk analysis as a field and discipline is about concepts, principles, approaches, methods, and models for understanding, assessing, communicating, managing, and governing risk. The foundation of this field and discipline has been subject to continuous discussion since its origin some 40 years ago with the establishment of the Society for Risk Analysis and the Risk Analysis journal. This article provides a perspective on critical foundational challenges that this field and discipline faces today, for risk analysis to develop and have societal impact. Topics discussed include fundamental questions important for defining the risk field, discipline, and science; the multidisciplinary and interdisciplinary features of risk analysis; the interactions and dependencies with other sciences; terminology and fundamental principles; and current developments and trends, such as the use of artificial intelligence.
Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs
Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?
The United States is funding homeland security programs with a large budget (e.g., 74.4 billion for FY 2019). A number of game‐theoretic defender–attacker models have been developed to study the optimal defense resource allocation strategies for the government (defender) against the strategic adversary (attacker). However, to the best of our knowledge, the substitution or complementary effects between different types of defensive resources (e.g., human resource, land resource, and capital resource) have not been taken into consideration even though they exist in practice. The article fills this gap by studying a sequential game‐theoretical resource allocation model and then exploring how the joint effectiveness of multiple security investments influences the defensive budget allocation among multiple potential targets. Three false belief models have been developed in which only the defender, only the attacker, and both the defender and attacker hold false beliefs about the joint effectiveness of resources. Regression analysis shows that there are significant substitution effects between human and capital resources. The results show that the defender will suffer a higher loss if he fails to consider the substitution or complementary effects. Interestingly, if the attacker holds a false belief while the defender does not, the defender will suffer an even higher loss, especially when the resources are substitutes. However, if both the attacker and defender hold false beliefs, there will be lower loss when resources are complementary. The results also show that the defender should allocate the highly effective resource when the resources substitute each other. This article provides some new insights to the homeland security resource allocation.
Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision‐making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S‐OLAP) technology. Although the S‐OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well‐known multicriteria classification method, the dominance‐based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S‐OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.
The concept of risk has received scholarly attention from a variety of angles in the social, technical, and natural sciences. However, public policy scholars have not yet generated a comprehensive overview, shared understanding and conceptual framework of the main problem‐solving approaches applied by governments in coping with risks. In this regard, our main aim is to examine existing perspectives on prevailing risk coping strategies, find a common denominator among them and contribute to current policy and risk science literature through providing a conceptual framework that systematically spans the spectrum of risk coping strategies and incorporates the essence of the most relevant insights. To this end, we first examine the concept of risk in‐depth by exploring various definitions and types of risk. We then review different approaches proposed by different strands of research for addressing risk. Finally, we assess current knowledge and develop an amalgamated perspective for examining how risks can be addressed by classifying them into six general types of response (no response; prevention; control; precaution; toleration; and adaptation) as well as indicators to identify these responses. We argue that these strategies can function as a heuristic tool for decisionmakers in designing appropriate policies to cope with risks in decision‐making processes.
Safe‐by‐Design: Stakeholders’ Perceptions and Expectations of How to Deal with Uncertain Risks of Emerging Biotechnologies in the Netherlands
Advanced gene editing techniques such as Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas have increased the pace of developments in the field of industrial biotechnology. Such techniques imply new possibilities when working with living organisms, possibly leading to uncertain risks. In the Netherlands, current policy fails to address these uncertain risks because risk classification is determined process‐wise (i.e., genetically modified organism [GMO] and non‐GMO), there is a strong focus on quantifiable risks, and the linearity within current governance (science–policy–society) hinders iterative communication between stakeholders, leaving limited room to anticipate uncertainties at an early stage of development. A suggested concept to overcome these shortcomings is the Safe‐by‐Design (SbD) approach, which, theoretically, allows stakeholders to iteratively incorporate safety measures throughout a technology's development process, creating a dynamic environment for the anticipation of uncertain risks. Although this concept originates from chemical engineering and is already widely applied in nanotechnology, for the field of biotechnology, there is no agreed upon definition yet. To explore the possibilities of SbD for future governance of biotechnology, we should gain insight in how various stakeholders perceive notions of risk, safety, and inherent safety, and what this implies for the applicability of SbD for risk governance concerning industrial biotechnology. Our empirical research reveals three main themes: (1) diverging expectations with regard to safety and risks, and to establish an acceptable level of risk; (2) different applications of SbD and inherent safety, namely, product‐ and process‐wise; and (3) unclarity in allocating responsibilities to stakeholders in the development process of a biotechnology and within society.
The World Health Organization has declared antibiotic resistance “one of the biggest threats to global health.” Mounting evidence suggests that antibiotic use in industrial‐scale hog farming is contributing to the spread of antibiotic‐resistant Staphylococcus aureus. To capture available evidence on these risks, we searched peer‐reviewed studies published before June 2017 and conducted a meta‐analysis of these studies’ estimates of the prevalence of swine‐associated, antibiotic‐resistant S. aureus in animals, humans, and the environment. The 166 relevant studies revealed consistent evidence of livestock‐associated methicillin‐resistant S. aureus (MRSA) in hog herds (55.3%) raised with antibiotics. MRSA prevalence was also substantial in slaughterhouse pigs (30.4%), industrial hog operation workers (24.4%), and veterinarians (16.8%). The prevalence of swine‐associated, multidrug‐resistant S. aureus (MDRSA)—with resistance to three or more antibiotics—is not as well documented. Nonetheless, sufficient studies were available to estimate MDRSA pooled prevalence in conventional hog operation workers (15.0%), workers’ household members (13.0%), and community members (5.37%). Evidence also suggests that antibiotic‐resistant S. aureus can be present in air, soil, water, and household surface samples gathered in or near high‐intensity hog operations. An important caveat is that prevalence estimates for humans reflect colonization, not active infection, and the health risks of colonization remain poorly understood. In addition, these pooled results may not represent risks in specific locations, due to wide geographic variation. Nonetheless, these results underscore the need for additional preventive action to stem the spread of antibiotic‐resistant pathogens from livestock operations and a streamlined reporting system to track this risk.
Maintaining the performance of infrastructure‐dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first‐order system qualities remain elusive in the field. An integrative understanding of how such conditions co‐occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure‐dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self‐organizing nature of infrastructure‐dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social‐ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience‐enhancing principles for infrastructure‐dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co‐occur and their interdependencies.
We urgently need to put the concept of resilience into practice if we are to prepare our communities for climate change and exacerbated natural hazards. Yet, despite the extensive discussion surrounding community resilience, operationalizing the concept remains challenging. The dominant approaches for assessing resilience focus on either evaluating community characteristics or infrastructure functionality. While both remain useful, they have several limitations to their ability to provide actionable insight. More importantly, the current conceptualizations do not consider essential services or how access is impaired by hazards. We argue that people need access to services such as food, education, health care, and cultural amenities, in addition to water, power, sanitation, and communications, to get back some semblance of normal life. Providing equitable access to these types of services and quickly restoring that access following a disruption are paramount to community resilience. We propose a new conceptualization of community resilience that is based on access to essential services. This reframing of resilience facilitates a new measure of resilience that is spatially explicit and operational. Using two illustrative examples from the impacts of Hurricanes Florence and Michael, we demonstrate how decisionmakers and planners can use this framework to visualize the effect of a hazard and quantify resilience‐enhancing interventions. This “equitable access to essentials” approach to community resilience integrates with spatial planning, and will enable communities not only to “bounce back” from a disruption, but to “bound forward” and improve the resilience and quality of life for all residents.
Incorporating Message Framing into Narrative Persuasion to Curb E‐Cigarette Use Among College Students
This study examines the interaction effect of message format (narrative vs. nonnarrative) and message framing (gain vs. loss) in e‐cigarette prevention targeting young adults. Results of a two‐way experiment (N = 439) revealed that transportation and discrete emotions mediated message effect on risk perception and behavioral intention. Compared to the gain‐framed nonnarrative, the gain‐framed narrative reduced feelings of guilt, and guilt was negatively related to risk perception and positively related to behavioral intention. Thus, the gain‐framed narrative achieved desirable persuasive outcome through guilt—increasing risk perception and decreasing intention to use e‐cigarette. Similarly, the loss‐framed narrative evoked greater sadness, which also led to increased risk perception and decreased behavioral intention. Transportation and discrete emotions mediated message effect in a serial order. This research not only contributes to the literature on narrative persuasion and emotion, but also provides insight for health communication designed for e‐cigarette prevention.
Uncertain Risk Assessment and Management: Case Studies of the Application of the Precautionary Principle in Portugal
This study intends to clarify how the precautionary principle (PP) has been interpreted and applied by the courts in Portugal in the analysis of conflicts associated with uncertain and serious potential risks to human health and the environment. It also aims to contribute to the debate of when and how to apply precautionary measures. To this end, recent court cases in the areas of waste incineration, high‐voltage power lines, as well as dam and wind farm construction were considered. The degree of consistency in the courts’ decisions and their reasons in the different judicial bodies was analyzed with the support of a theoretical framework based on three attributes: the level of seriousness of potential hazards, level of evidence required, and the severity of precautionary actions taken. Different positions among courts were observed, with contradictory arguments in the same case or in similar cases. A greater propensity for favorable decisions in the acceptance of restraining orders was verified in the courts of lower instances, where human health could be threatened. However, the decisions of the Supreme Administrative Court, which were always unfavorable to the restraining orders, seem to reflect the priority given to national economic and political interests over local or regional environmental interests. They may also reflect the Supreme Court's reluctancy to apply the PP in the absence of a firm legally binding PP in national legislation. To address this situation, more explicit legal requirements and criteria for the analysis of uncertain risks and the weighting of interests by area of activity are needed.
Previous studies of risk behavior observed weak or inconsistent relationships between risk perception and risk‐taking. One aspect that has often been neglected in such studies is the situational context in which risk behavior is embedded: Even though a person may perceive a behavior as risky, the social norms governing the situation may work as a counteracting force, overriding the influence of risk perception. Three food context studies are reported. In Study 1 (N = 200), we assess how norm strength varies across different social situations, relate the variation in norm strength to the social characteristics of the situation, and identify situations with consistently low and high levels of pressure to comply with the social norm. In Study 2 (N = 502), we investigate how willingness to accept 15 different foods that vary in terms of objective risk relates to perceived risk in situations with low and high pressure to comply with a social norm. In Study 3 (N = 1,200), we test how risk‐taking is jointly influenced by the perceived risk associated with the products and the social norms governing the situations in which the products are served. The results indicate that the effects of risk perception and social norm are additive, influencing risk‐taking simultaneously but as counteracting forces. Social norm had a slightly stronger absolute effect, leading to a net effect of increased risk‐taking. The relationships were stable over different social situations and food safety risks and did not disappear when detailed risk information was presented.
Evacuation is frequently used by emergency managers and other officials as part of an overall approach to reducing the morbidity and mortality associated with hurricane landfall. In this study, the evacuation shelter capacity of the Houston–Galveston Metropolitan Statistical Area (MSA) was spatially assessed and shelter deficits in the region were estimated. These data provide essential information needed to eliminate shelter deficits and ensure a successful evacuation from a future storm. Spatial statistical methods—Global Moran's I, Anselin Local Moran's I (Local Indicators of Spatial Association [LISA]), and Hot Spot Analysis (Getis‐Ord Gi*) were used to assess for regional spatial autocorrelation and clustering of evacuation shelters in the Houston–Galveston MSA. Shelter deficits were estimated in four ways—the aggregate deficit for the Houston–Galveston MSA, by evacuation Zip‐Zone, by county, and by distance or radii of evacuation Zip‐Zone. Evacuation shelters were disproportionately distributed in the region, with lower capacity shelters clustered closer to evacuation Zip‐Zones (50 miles from the Coastal Zip‐Zone), and higher capacity shelters clustered farther away from the zones (120 miles from the Coastal Zip‐Zone). The aggregate shelter deficit for the Houston–Galveston MSA was 353,713 persons. To reduce morbidity and mortality associated with future hurricanes in the Houston–Galveston MSA, authorities should consider the development and implementation of policies that would improve the evacuation shelter capacity of the region. Eliminating shelter deficits, which has been done successfully in the state of Florida, is an essential element of protecting the public from hurricane impacts.
According to the class of de minimis decision principles, risks can be ignored (or at least treated very differently from other risks) if the risk is sufficiently small. In this article, we argue that a de minimis threshold has no place in a normative theory of decision making, because the application of the principle will either recommend ignoring risks that should not be ignored (e.g., the sure death of a person) or it cannot be used by ordinary bounded and information‐constrained agents.
Natural hazards pose an increasing challenge to public administrators, as the frequency, costs, and consequences of extreme events escalate in a complex, interdependent, world. This study examines organizational networks as instruments for mobilizing collective response to extreme events, but effective design has been elusive. Governments have focused on planned networks to anticipate risk before hazards occur; communities have formed emergent networks as voluntary efforts after the event. Using a framework of complex adaptive systems, we identify operational networks that adapt to their immediate context in real time, using technologies to support the search, exchange, and feedback of information to enable informed, collective action. Applying mixed research methods—documentary analysis of laws, policies, and procedures; content analysis of news articles; onsite observation; and semistructured interviews with experienced personnel—we document operational networks as a distinct form of multiorganizational response to urgent events that combines the structure of designated authority with the flexibility of information technologies. The integration of planned and emergent organizational forms into operational networks is measured through External/Internal (E/I) index analysis, based on empirical data collected on response systems that formed following the 2008 Wenchuan and 2013 Lushan earthquakes in the centralized administrative context of China. Findings show that planned networks provide the organizational structure and initial legitimacy essential for operational networks to form, but ready access to information technology—cell phones, short‐wave radio systems, internet access—enables rapid communication and exchange of information essential for flexible adaptation in real time to meet urgent needs.
Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow‐ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.