Decision Making Under Uncertainty—An Example for Seismic Risk Management

Decision-making techniques are used to select the "best" alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision-making process. The major thrust of this article is to extend the framework proposed by Yager^{( 1 )} for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step-by-step formulation of the proposed approach is illustrated using a hypothetical example and a three-story reinforced concrete building.     

Risk Assessment and Risk Governance of Liquefied Natural Gas Development in Gladstone,Australia

This article is a retrospective analysis of liquefied natural gas development (LNG) in Gladstone, Australia by using the structure of the risk governance framework developed by the International Risk Governance Council (IRGC). Since 2010 the port of Gladstone has undergone extensive expansion to facilitate the increasing coal export as well as the new development of three recently completed LNG facilities. Significant environmental and socio‐economic impacts and concerns have occurred as a result of these developments. The overall aim of the article, therefore, is to identify the risk governance deficits that arose and to formulate processes capable of improving similar decision‐making problems in the future. The structure of the IRGC framework is followed because it represents a broad analytical approach for considering risk assessment and risk governance in Gladstone in ways that include, but also go beyond, the risk approach of the ISO 31000:2009 standard that was employed at the time. The IRGC risk framework is argued to be a consistent and comprehensive risk governance framework that integrates scientific, economic, social, and cultural aspects and advocates the notion of inclusive risk governance through stakeholder communication and involvement. Key aspects related to risk preassessment, risk appraisal, risk tolerability and acceptability, risk management, and stakeholder communication and involvement are considered. The results indicate that the risk governance deficits include aspects related to (i) the risk matrix methodology, (ii) reflecting uncertainties, (iii) cumulative risks, (iv) the regulatory process, and (v) stakeholder communication and involvement.     

Operational Risk in Financial Services: A Review and New Research Opportunities

We present a framework to describe and analyze operational risk in financial services from an operations management perspective, focusing in particular on process design, process management, and human behavior aspects. The financial services industry differs from other service industries in ways that affect the nature of the operational risks it is subject to. In recent decades, many books and papers have focused on operational risk in financial services; however, this literature has focused mainly on the conceptual and statistical aspects of operational risk management and not on its operational aspects. Operational risk in financial services has not received much attention from the operations management community. The framework presented here is based on the premise that operational risk in financial services can reap significant benefits from research done in the theory and practice of operations management in manufacturing industries as well as in other services industries. The objective of this study is to propose particular challenges and questions raised in the practice of operational risk management that may stimulate future research in this particular area of operations management.     

Confluence and Contours: Reflexive Management of Environmental Risk

Government institutions have responsibilities to distribute risk management funds meaningfully and to be accountable for their choices. We took a macro‐level sociological approach to understanding the role of government in managing environmental risks, and insights from micro‐level psychology to examine individual‐level risk‐related perceptions and beliefs. Survey data from 2,068 U.K. citizens showed that lay people's funding preferences were associated positively with beliefs about responsibility and trust, yet associations with perception varied depending on risk type. Moreover, there were risk‐specific differences in the funding preferences of the lay sample and 29 policymakers. A laboratory‐based study of 109 participants examined funding allocation in more detail through iterative presentation of expert information. Quantitative and qualitative data revealed a meso‐level framework comprising three types of decisionmakers who varied in their willingness to change funding allocation preferences following expert information: adaptors, responders, and resistors. This research highlights the relevance of integrated theoretical approaches to understanding the policy process, and the benefits of reflexive dialogue to managing environmental risks.     

Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases

A novel approach to the quantitative assessment of food-borne risks is proposed. The basic idea is to use Bayesian techniques in two distinct steps: first by constructing a stochastic core model via a Bayesian network based on expert knowledge, and second, using the data available to improve this knowledge. Unlike the Monte Carlo simulation approach as commonly used in quantitative assessment of food-borne risks where data sets are used independently in each module, our consistent procedure incorporates information conveyed by data throughout the chain. It allows "back-calculation" in the food chain model, together with the use of data obtained "downstream" in the food chain. Moreover, the expert knowledge is introduced more simply and consistently than with classical statistical methods. Other advantages of this approach include the clear framework of an iterative learning process, considerable flexibility enabling the use of heterogeneous data, and a justified method to explore the effects of variability and uncertainty. As an illustration, we present an estimation of the probability of contracting a campylobacteriosis as a result of broiler contamination, from the standpoint of quantitative risk assessment. Although the model thus constructed is oversimplified, it clarifies the principles and properties of the method proposed, which demonstrates its ability to deal with quite complex situations and provides a useful basis for further discussions with different experts in the food chain.     

Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments

Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicle's life history of 63 faults and incidents. After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan‐Meier estimator. We present theoretical formulations for the estimator, its variance, and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under Pine Island Glacier, Antarctica in January–March 2009.     

Flood Risk Management: US Army Corps of Engineers and Layperson Perceptions

Recent severe storm experiences in the U.S. Gulf Coast illustrate the importance of an integrated approach to flood preparedness planning that harmonizes stakeholder and agency efforts. Risk management decisions that are informed by and address decision maker and stakeholder risk perceptions and behavior are essential for effective risk management policy. A literature review and two expert models/mental models studies were undertaken to identify areas of importance in the flood risk management process for layperson, non‐USACE‐expert, and two USACE‐expert groups. In characterizing and mapping stakeholder beliefs about risks in the literature onto current risk management practice, recommendations for accommodating and changing stakeholder perceptions of flood risks and their management are identified. Needs of the U.S. Army Corps of Engineers (USACE) flood preparedness and response program are discussed in the context of flood risk mental models.     

The Mutable Nature of Risk and Acceptability: A Hybrid Risk Governance Framework

This article focuses on the fluid nature of risk problems and the challenges it presents to establishing acceptability in risk governance. It introduces an actor‐network theory (ANT) perspective as a way to deal with the mutable nature of risk controversies and the configuration of stakeholders. To translate this into a practicable framework, the article proposes a hybrid risk governance framework that combines ANT with integrative risk governance, deliberative democracy, and responsive regulation. This addresses a number of the limitations in existing risk governance models, including: (1) the lack of more substantive public participation throughout the lifecycle of a project; (2) hijacking of deliberative forums by particular groups; and (3) the treatment of risk problems and their associated stakeholders as immutable entities. The framework constitutes a five‐stage process of co‐selection, co‐design, co‐planning, and co‐regulation to facilitate the co‐production of collective interests and knowledge, build capacities, and strengthen accountability in the process. The aims of this article are twofold: conceptually, it introduces a framework of risk governance that accounts for the mutable nature of risk problems and configuration of stakeholders. In practice, this article offers risk managers and practitioners of risk governance a set of procedures with which to operationalize this conceptual approach to risk and stakeholder engagement.     

Decidedly Different: Expert and Public Views of Risks from a Radioactive Waste Repository

A questionnaire with items that had been used in a national survey of the general public was administered to persons attending an American Nuclear Society meeting. The items asked about risks associated with high-level nuclear waste (HLNW), trust in nuclear-waste program managers, costs and benefits of a repository project, and images of a HLNW repository. The results suggest that nuclear industry experts may have very different opinions from the general public about most of these items and their images of a repository indicate a vastly different conceptual framework within which their opinions are formed.     

Risk Framework for an Organizational System With Major Components

Many large organizations accomplish their various functions through interactions across their major components. Components refers to functional entities within a large complex organization, such as business sectors, academic departments, or regional divisions. The dependency between the various components can cause risk to propagate through their overall system. This article presents a risk assessment framework that integrates risk across a diverse set of components to the overall organization functions. This project addresses three major challenges: aggregating risk, estimating component interdependencies including cycles of dependencies, and propagating risk across components. The framework aggregates risk assessments through a value function for severity that is evaluated at the expected outcome of accomplishing planned goals in terms of performance, schedule, and resources. The value function, which represents risk tolerance, scales between defined points corresponding to failure and success. Different risk assessment may be aggregated together. This article presents a novel approach to establishing relationships between the various components. This article develops and compares three network risk propagation models that characterize the overall organizational risk. The U.S. Air Force has applied this risk framework to evaluate success in hypothetical future wars. The analysts employing this risk framework have informed billions of dollars of strategic investment decisions.     

Using Bayesian Networks to Model Expected and Unexpected Operational Losses

This report describes the use of Bayesian networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its focus is on modeling "long" tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying controls process. The use of causal modeling is discussed from the perspective of exploiting local expertise about process reliability and formally connecting this knowledge to actual or hypothetical statistical phenomena resulting from the process. This brings the benefit of supplementing sparse data with expert judgment and transforming qualitative knowledge about the process into quantitative predictions. We conclude that BNs can help combine qualitative data from experts and quantitative data from historical loss databases in a principled way and as such they go some way in meeting the requirements of the draft Basel II Accord (Basel, 2004) for an advanced measurement approach (AMA).     

Decision analysis and risk models for land development affecting infrastructure systems

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development.     

The Essential Elements of a Risk Governance Framework for Current and Future Nanotechnologies

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk‐based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano‐specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology.     

Social Amplification of Wildfire Risk: The Role of Social Interactions and Information Sources

Wildfire is a persistent and growing threat across much of the western United States. Understanding how people living in fire‐prone areas perceive this threat is essential to the design of effective risk management policies. Drawing on the social amplification of risk framework, we develop a conceptual model of wildfire risk perceptions that incorporates the social processes that likely shape how individuals in fire‐prone areas come to understand this risk, highlighting the role of information sources and social interactions. We classify information sources as expert or nonexpert, and group social interactions according to two dimensions: formal versus informal, and generic versus fire‐specific. Using survey data from two Colorado counties, we empirically examine how information sources and social interactions relate to the perceived probability and perceived consequences of a wildfire. Our results suggest that social amplification processes play a role in shaping how individuals in this area perceive wildfire risk. A key finding is that both “vertical” (i.e., expert information sources and formal social interactions) and “horizontal” (i.e., nonexpert information and informal interactions) interactions are associated with perceived risk of experiencing a wildfire. We also find evidence of perceived “risk interdependency”—that is, homeowners’ perceptions of risk are higher when vegetation on neighboring properties is perceived to be dense. Incorporating social amplification processes into community‐based wildfire education programs and evaluating these programs’ effectiveness constitutes an area for future inquiry.     

A Bayesian Approach to Integrated Ecological and Human Health Risk Assessment for the South River,Virginia Mercury‐Contaminated Site

We conducted a regional‐scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN‐RRM) to a case study of the South River, Virginia mercury‐contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor–multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN‐RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape.     

A Study of Risk Relevance Reasoning Based on a Context Ontology of Railway Accidents

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.     

Perceived Risks from Radiation and Nuclear Testing Near Semipalatinsk, Kazakhstan: A Comparison Between Physicians, Scientists, and the Public

Determining the difference in perception of risk between experts, or more educated professionals, and laypeople is important so that a potential hazard can be effectively communicated to the public. Many surveys have been conducted to better understand the difference between expert and public opinions, and often laypeople exhibit higher perceptions of risk to hazards in comparison to experts. This is especially true when health risk is due to radiation, nuclear power, and nuclear waste. This article focuses on one section of a risk perception survey given to two groups of individuals with a more specialized education (scientists and physicians) and laypeople (villagers) in the Semipalatinsk region of Kazakhstan. All of these groups live near the former Soviet nuclear test site. Originally, it was expected that the scientists and physicians would have similar perceptions of radiation risk, while the public perceptions would be higher, but this was not always the case. For example, when perceptions of risk pertain to the health impacts of nuclear testing or the dose-response nature of radiation exposure, the physicians tend to agree with the laypeople, not the scientists. The villagers are always the most risk-averse group, followed by the physicians and then the scientists. These differences are likely due to different frames of reference for each of the populations.     

股市收益率高阶矩风险的产生机制检验

通过对现有理论文献的梳理,提炼了五个较为典型的关于高阶矩风险产生机制的理论假设。然后基于时变高阶矩建模思想,将这五个假设统一于同一个计量框架,并进行综合地实证检验,以期发掘具有"占优"作用的理论解释。以沪深股市收益率为样本研究发现,在这五个假设中,仅"波动率反馈"效应和"利空信息揭示"效应获得显著的实证支持。进一步分析指出,这两种效应会同时引起偏度和峰度风险,因而是高阶矩风险的主要产生机制。这一结果可为后续研究发展一个统一的理论框架提供实证参考。     

Multicriteria Decision Framework for Cybersecurity Risk Assessment and Management

Risk assessors and managers face many difficult challenges related to novel cyber systems. Among these challenges are the constantly changing nature of cyber systems caused by technical advances, their distribution across the physical, information, and sociocognitive domains, and the complex network structures often including thousands of nodes. Here, we review probabilistic and risk-based decision-making techniques applied to cyber systems and conclude that existing approaches typically do not address all components of the risk assessment triplet (threat, vulnerability, consequence) and lack the ability to integrate across multiple domains of cyber systems to provide guidance for enhancing cybersecurity. We present a decision-analysis-based approach that quantifies threat, vulnerability, and consequences through a set of criteria designed to assess the overall utility of cybersecurity management alternatives. The proposed framework bridges the gap between risk assessment and risk management, allowing an analyst to ensure a structured and transparent process of selecting risk management alternatives. The use of this technique is illustrated for a hypothetical, but realistic, case study exemplifying the process of evaluating and ranking five cybersecurity enhancement strategies. The approach presented does not necessarily eliminate biases and subjectivity necessary for selecting countermeasures, but provides justifiable methods for selecting risk management actions consistent with stakeholder and decisionmaker values and technical data.     

The [R]Evolving Relationship Between Risk Assessment and Risk Management

In Science and Decisions: Advancing Risk Assessment, the National Research Council recommends improvements in the U.S. Environmental Protection Agency's approach to risk assessment. The recommendations aim to increase the utility of these assessments, embedding them within a new risk‐based decision‐making framework. The framework involves first identifying the problem and possible options for addressing it, conducting related analyses, then reviewing the results and making the risk management decision. Experience with longstanding requirements for regulatory impact analysis provides insights into the implementation of this framework. First, neither the Science and Decisions framework nor the framework for regulatory impact analysis should be viewed as a static or linear process, where each step is completed before moving on to the next. Risk management options are best evaluated through an iterative and integrative procedure. The extent to which a hazard has been previously studied will strongly influence analysts’ ability to identify options prior to conducting formal analyses, and these options will be altered and refined as the analysis progresses. Second, experience with regulatory impact analysis suggests that legal and political constraints may limit the range of options assessed, contrary to both existing guidance for regulatory impact analysis and the Science and Decisions recommendations. Analysts will need to work creatively to broaden the range of options considered. Finally, the usefulness of regulatory impact analysis has been significantly hampered by the inability to quantify many health impacts of concern, suggesting that the scientific improvements offered within Science and Decisions will fill an crucial research gap.