Risk Analysis最新文献

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.

Europe faces regular introductions and reintroductions of bluetongue virus (BTV) serotypes, most recently exemplified by the incursion of serotype 3 in the Netherlands. Although the long-distance wind dispersal of the disease vector, Culicoides spp., is recognized as a virus introduction pathway, it remains understudied in risk assessments. A Quantitative Risk Assessment framework was developed to estimate the risk of BTV-3 incursion into mainland Europe from Sardinia, where the virus has been present since 2018. We used an atmospheric transport model (HYbrid Single-Particle Lagrangian Integrated Trajectory) to infer the probability of airborne dispersion of the insect vector. Epidemiological disease parameters quantified the virus prevalence in vector population in Sardinia and its potential first transmission after introduction in a new area. When assuming a 24h maximal flight duration, the risk of BTV introduction from Sardinia is limited to the Mediterranean Basin, mainly affecting the southwestern area of the Italian Peninsula, Sicily, Malta, and Corsica. The risk extends to the northern and central parts of Italy, Balearic archipelago, and mainland France and Spain, mostly when maximal flight duration is longer than 24h. Additional knowledge on vector flight conditions and Obsoletus complex-specific parameters could improve the robustness of the model. Providing both spatial and temporal insights into BTV introduction risks, our framework is a key tool to guide global surveillance and preparedness against epizootics.

Critical infrastructures are ubiquitous and their interdependencies have become more complex leading to their uncertain behaviors in the aftermath of disasters. The article develops an integrated economic input-output model that incorporates household-level survey data from Hurricane Sandy, which made its landfall in 2012. In this survey, 427 respondents who were living in the state of New Jersey during Hurricane Sandy were used in the study. The integration of their responses allowed us to show the probability and duration of various types of critical infrastructure failures due to a catastrophic hurricane event and estimate the economic losses across different sectors. The percentage of disruption and recovery period for various infrastructure systems were extracted from the survey, which were then utilized in the economic input-output model comprising of 71 economic sectors. Sectors were then ranked according to: (i) inoperability, the percentage in which a sector is disrupted relative to its ideal level, and (ii) economic loss, the monetary worth of business interruption caused by the disaster. With the combined infrastructure disruptions in the state of New Jersey, the model estimated an economic loss of $36 billion, which is consistent with published estimates. Results from this article can provide insights for future disaster preparedness and resilience planning.

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision-maker's interest may not converge with the others. Subsequently, this results in each decision-maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision-makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.

Nonrenewable energy sources have been shown to be a cause of conflict and terrorism, highlighting the global conflict aspect, but little is known about the causal relationship between the energy system and terrorism in Turkey. This study aims to fill this gap by examining the causal links among renewable energy consumption, fossil fuels, terrorist attacks, education, trade opening, and geopolitical risks in Turkey from 1980 to 2016. Using the autoregressive distributed lag (ARDL) approach and Granger causality tests, the study analyzes the short and long-term relationships between the variables. Additionally, robustness tests are conducted using a powerful multiresolution ARDL approach to ensure the stability of the statistical findings. The results reveal the existence of long-term relationships between all the variables, particularly among terrorism, renewable energy, and education. In the short term, a one-way relationship exists between terrorism and education to renewable energies and from trade openness to terrorism. The study demonstrates that nonrenewable energy increases terrorism in the long term, whereas renewable energy and trade openness reduce terrorism, highlighting the potential impact of global conflicts on Turkey's sustainable development. Therefore, renewable energy is a powerful tool to fight against terrorism, and Turkey has encouraged its use and deployment of diplomatic efforts to resolve political and military conflicts, particularly in the Middle East. This study provides insights into the complex relationship among sustainable energy consumption, terrorism, education, and trade opening, contributing to the understanding of the geopolitical risks and economics in Turkey. It has implications for policymakers in the region, highlighting the importance of renewable energy and trade openness as tools for conflict resolution and sustainable development in the face of global conflicts.

Risk analysis has existed for thousands of years and will continue to grow in importance across professions and industries. Of special importance is the need to understand and manage risk when there is low knowledge and high uncertainties. Even with pristine and high-quality risk analysis in these situations, integrity and credibility can be questioned, and risk events can happen. Although these issues do not prove some shortcoming in risk analysis and risk management, they can directly impact the risk analyst and decision-makers. The risk literature has addressed the issues of defining and promoting integrity and credibility for risk studies, but there is little existing guidance for the analyst when handling the commonly encountered low knowledge and high uncertainty contexts. In this article, we explore the implications of low knowledge and high uncertainty in risk studies to understand how the risk analyst can acknowledge those features in a risk study, with recognition that those features may be questioned later. The topic of this article will be of interest to risk managers, professionals, and analysts in general who are tasked with analyzing and communicating with studies.

As today's engineering systems have become increasingly sophisticated, assessing the efficacy of their safety-critical systems has become much more challenging. The more classical methods of "failure" analysis by decomposition into components related by logic trees, such as fault and event trees, root cause analysis, and failure mode and effects analysis lead to models that do not necessarily behave like the real systems they are meant to represent. These models need to display similar emergent and unpredictable behaviors to sociotechnical systems in the real world. The question then arises as to whether a return to a simpler whole system model is necessary to understand better the behavior of real systems and to build confidence in the results. This question is more prescient when one considers that the causal chain in many serious accidents is not as deep-rooted as is sometimes claimed. If these more obvious causes are not taken away, why would the more intricate scenarios that emanate from more sophisticated models be acted upon. The paper highlights the advantages of modeling and analyzing these "normal" deviations from ideality, so called weak signals, versus just system failures and near misses as well as catastrophes. In this paper we explore this question.

The World Health Organization (WHO) officially declared COVID-19 a pandemic on March 11, 2020. It was a time of significant uncertainty as experts were not yet certain whether social distancing behaviors were necessary to slow the spread of the virus. Some public communicators opted to acknowledge uncertainty based on the limited evidence, whereas others downplayed uncertainty. This situation provided researchers with an opportunity to advance theory by explicating and testing cognitive responses to message uncertainty. Immediately following the WHO declaration (March 13-19, 2020), U.S. adults (N = 1186) were randomly assigned to one of six conditions in a 2 (message uncertainty: low, high) × 3 (argument support: expert, threat, precedent) between-participants experiment. Overall, perceived uncertainty negatively mediated the impact of message uncertainty on intentions. However, participant education was a key moderator. For those with more than a high school education, uncertain messages were related to higher intentions to social distance through increased critical reflection. For those with a high school education or less, uncertain messages were related to lower intentions through decreased message credibility.

Recent history has shown both the benefits and risks of information sharing among firms. Information is shared to facilitate mutual business objectives. However, information sharing can also introduce security-related concerns that could expose the firm to a breach of privacy, with significant economic, reputational, and safety implications. It is imperative for organizations to leverage available information to evaluate security related to information sharing when evaluating current and potential information-sharing partnerships. The "fine print" or privacy policies of firms can provide a signal of security across a wide variety of firms being considered for new and continued information-sharing partnerships. In this article, we develop a methodology to gauge and benchmark information security policies in the partner-selection process that can help direct risk-based investments in information sharing security. We develop a methodology to collect and interpret firm privacy policies, evaluate characteristics of those policies by leveraging natural language processing metrics and developing benchmarking metrics, and understand how those characteristics relate to one another in information-sharing partnership situations. We demonstrate the methodology on 500 high-revenue firms. The methodology and managerial insights will be of interest to risk managers, information security professionals, and individuals forming information sharing agreements across industries.