Confidence deficits and reducibility: Toward a coherent conceptualization of uncertainty level

Outside of the field of risk analysis, an important theoretical conversation on the slippery concept of uncertainty has unfolded over the last 40 years within the adjacent field of environmental risk. This literature has become increasingly standardized behind the tripartite distinction between uncertainty location, the nature of uncertainty, and uncertainty level, popularized by the “W&H framework.” This article introduces risk theorists and practitioners to the conceptual literature on uncertainty with the goal of catalyzing further development and clarification of the uncertainty concept within the field of risk analysis. It presents two critiques of the W&H framework's dimension of uncertainty level—the dimension that attempts to define the characteristics separating greater uncertainties from lesser uncertainties. First, I argue the framework's conceptualization of uncertainty level lacks a clear and consistent epistemological position and fails to acknowledge or reconcile the tensions between Bayesian and frequentist perspectives present within the framework. This article reinterprets the dimension of uncertainty level from a Bayesian perspective, which understands uncertainty as a mental phenomenon arising from “confidence deficits” as opposed to the ill-defined notion of “knowledge deficits” present in the framework. And second, I elaborate the undertheorized concept of uncertainty “reducibility.” These critiques inform a clarified conceptualization of uncertainty level that can be integrated with risk analysis concepts and usefully applied by modelers and decisionmakers engaged in model-based decision support.     

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis

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.     

Risk Analysis,Decision Analysis,Causal Analysis,and Economics: A Personal Perspective from More Than 40 years Experience

I entered the field of risk analysis forty years ago from a background in physics followed by doctoral training and experience in decision analysis. I came into the Society for Risk Analysis (SRA) after participating as a committee member in the 1983 National Academies report, Risk Assessment in the Federal Government: Managing the Process. The insights and recommendations from this report, and successor reports from 1996 and 2008, merit revisiting on this 40th anniversary. Risk analysis includes risk assessment, a process of summarizing applicable science to inform decisions; and risk management, a process of making informed choices, usually involving multiple stakeholders. Inherent in both is the need to deal with complexity, uncertainty, and differing perspectives and goals. The lessons I have learned include the need for a conceptual separation of risk management from risk assessment, the benefit of an iterative dialogue between these activities, and the wisdom of articulating and assessing what we know, what we want, and what we can do as we seek to understand and manage risks affecting ourselves and those we advise.     

What Defines Us as Professionals in the Field of Risk Analysis?

In a recent issue of Risk Analysis, the then‐President of the Society for Risk Analysis (SRA), Pamela Williams, has some interesting reflections about the risk analysis field. She states that the ability and desire to tackle difficult problems using a risk analytical approach is what uniquely defines us as professionals in the field of risk analysis. The point of departure for her discussion is interviews with the plenary speakers of the 2014 SRA Annual Meeting, who addressed two divisive topics: hydraulic fracking and marijuana use. She points to several themes that invite contributions from the field of risk analysis, including: Has the full spectrum of potential risks and benefits been identified and weighted, and what are the risk tradeoffs or countervailing risks? Inspired by Williams's reflections, and by analyzing the issues raised in the interviews, this article seeks to clarify what our field is really providing. A main conclusion of the article is that it is essential to acknowledge that professionals in the field of risk analysis merely support the tackling of such problems, and that their genuine competence—that which distinguishes them from other professionals—lies in the risk analytical approach itself.     

The Blind Spot in Risk Ethics: Managing Natural Hazards

Many risk scholars recognize the importance of including ethical considerations in risk management. Risk ethics can provide in‐depth ethical analysis so that ethical considerations can be part of risk‐related decisions, rather than an afterthought to those decisions. In this article, I present a brief sketch of the field of risk ethics. I argue that risk ethics has a bias toward technological hazards, thereby overlooking the risks that stem from natural and semi‐natural hazards. In order to make a contribution to the field of risk research, risks ethics should broaden its scope to include natural and semi‐natural hazards and develop normative distribution criteria that can support decision making on such hazards.     

Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events

Renewed interest in precursor analysis has shown that the evaluation of near misses is an interdisciplinary effort, fundamental within the life of an organization for reducing operational risks and enabling accident prevention. The practice of precursor analysis has been a part of nuclear power plant regulation in the United States for over 25 years. During this time, the models used in the analysis have evolved from simple risk equations to quite complex probabilistic risk assessments. But, one item that has remained constant over this time is that the focus of the analysis has been on modeling the scenario using the risk model (regardless of the model sophistication) and then using the results of the model to determine the severity of the precursor incident. We believe that evaluating precursors in this fashion could be a shortcoming since decision making during the incident is not formally investigated. Consequently, we present the idea for an evaluation procedure that enables one to integrate current practice with the evaluation of decisions made during the precursor event. The methodology borrows from technologies both in the risk analysis and the decision analysis realms. We demonstrate this new methodology via an evaluation of a U.S. precursor incident. Specifically, the course of the incident is represented by the integration of a probabilistic risk assessment model (i.e., the risk analysis tool) with an influence diagram and the corresponding decision tree (i.e., the decision analysis tools). The results and insights from the application of this new methodology are discussed.     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

Integrated Analysis: Combining Risk and Economic Assessments While Preserving the Separation of Powers

This article presents a process for an integrated policy analysis that combines risk assessment and benefit-cost analysis. This concept, which explicitly combines the two types of related analyses, seems to contradict the long-accepted risk analysis paradigm of separating risk assessment and risk management since benefit-cost analysis is generally considered to be a part of risk management. Yet that separation has become a problem because benefit-cost analysis uses risk assessment results as a starting point and considerable debate over the last several years focused on the incompatibility of the use of upper bounds or "safe" point estimates in many risk assessments with benefit-cost analysis. The problem with these risk assessments is that they ignore probabilistic information. As advanced probabilistic techniques for risk assessment emerge and economic analysts receive distributions of risks instead of point estimates, the artificial separation between risk analysts and the economic/decision analysts complicates the overall analysis. In addition, recent developments in countervailing risk theory suggest that combining the risk and benefit-cost analyses is required to fully understand the complexity of choices and tradeoffs faced by the decisionmaker. This article also argues that the separation of analysis and management is important, but that benefit-cost analysis has been wrongly classified into the risk management category and that the analytical effort associated with understanding the economic impacts of risk reduction actions need to be part of a broader risk assessment process.     

Cognitive and Motivational Biases in Decision and Risk Analysis

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.     

The Return Period Analysis of Natural Disasters with Statistical Modeling of Bivariate Joint Probability Distribution

New features of natural disasters have been observed over the last several years. The factors that influence the disasters’ formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk‐based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.     

基于粗糙集的投标风险分析与规避决策模型研究——国家自然科学基金项目（70271031）回溯

回顾了国家自然科学基金项目"基于粗糙集的投标风险分析与规避决策模型研究(70271031)"的立项背景,介绍了本项目的主要研究内容与主要成果,以及本项目的代表性论文、论著。     

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.     

Risk and Uncertainty Analysis in Government Safety Decisions

Probabilistic risk analysis (PRA) can be an effective tool to assess risks and uncertainties and to set priorities among safety policy options. Based on systems analysis and Bayesian probability, PRA has been applied to a wide range of cases, three of which are briefly presented here: the maintenance of the tiles of the space shuttle, the management of patient risk in anesthesia, and the choice of seismic provisions of building codes for the San Francisco Bay Area. In the quantification of a risk, a number of problems arise in the public sector where multiple stakeholders are involved. In this article, I describe different approaches to the treatments of uncertainties in risk analysis, their implications for risk ranking, and the role of risk analysis results in the context of a safety decision process. I also discuss the implications of adopting conservative hypotheses before proceeding to what is, in essence, a conditional uncertainty analysis, and I explore some implications of different levels of "conservatism" for the ranking of risk mitigation measures.     

Interdisciplinary Vision: The First 25 Years of the Society for Risk Analysis (SRA), 1980–2005

In early 1979 Robert B. Cumming recognized the growing need for risk researchers and practitioners to publish their work in a dedicated professional journal. This led to the formation of an organization to support such a journal, with the Certificate of Incorporation for the Society for Risk Analysis (SRA) made official on August 28, 1980. The first issue of Risk Analysis: An International Journal appeared in March 1981. This article reviews the history of the SRA's first 25 years. It reviews the SRA's formation and growth, provides analyses of its major products (the journal, newsletter, conferences, and meetings), and discusses its impact. This article relies on published literature and a history of the SRA's first 20 years written by two of the authors. This history covers the SRA's many successes, which demonstrate the strength and vitality of the organization and provide optimism for its future. These successes include this journal, which published over 2,150 papers between March 1981 and this December 2005 issue. The successes also include its stable membership of approximately 2,000 members from 43 countries, well-attended annual meetings, and increasing support for true international growth as demonstrated by international risk forums like the World Congress on Risk held in Brussels in June 2003. Similarly, the history also covers the SRA's challenges and difficulties, with the recognition that these provide both an important context about the organization and the opportunity to learn from past experiences. These include the challenges associated with spin-off organizations that decreased the SRA membership in some disciplinary areas, notably in engineering and exposure assessment. This history also includes quantitative analyses of the contents of the first 25 years of Risk Analysis: An International Journal. The results show significant growth in the number of articles published each year, starting with approximately 30 articles published in the first few years to over 120 articles per year now. They also show a relatively even distribution of articles in the life, physical, and social sciences, which demonstrate the sustained commitment of the SRA and the journal to support interdisciplinary risk-related research. The SRA organizational structure currently includes two sections (SRA-Europe and SRA-Japan), 22 Chapters, and nine Specialty Groups, and the structure remains somewhat in flux. We present this history in five sections that cover major themes: (1) SRA formation, (2) membership and organization, (3) publications, (4) meetings, and (5) thematic issues. Like any organization of its size, the SRA boasts a long and diverse history, and no article can possibly capture it all. We hope that in documenting the first 25 years, we strengthen the SRA by providing some perspective on its roots and a rigorous quantitative analysis of some of its products.     

Summarizing Risk Using Risk Measures and Risk Indices

Our society is fascinated with risk in many different areas and disciplines. One of the main ways to describe and communicate the level of risk is through risk indices, which summarize risk using numbers or categories such as words, letters, or colors. These indices are used to communicate risks to the public, understand how risk is changing over time, compare among different risks, and support decision making. Given the different methods to construct risk indices, including flawed methods such as risk matrices, this article develops specific steps that analysts can follow to create a risk index. This article emphasizes the importance of describing risk with a probability distribution, developing a numerical risk measure that summarizes the probability distribution, and finally translating the risk measure to an index. Measuring the risk is the most difficult part and requires the analyst to summarize a probability distribution into one or possibly a few numbers. The risk measure can then be transformed to a numerical or categorical index. I apply the method outlined in this article to construct a risk index that compares the risk of fatalities in aviation and highway transportation.     

From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls “foundational issues.” In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor‐based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.     

Risk in Benefit-Cost Analysis

In recent years, benefit-cost analysis has been increasingly applied to large societal decision problems (such as developing a fast breeder energy economy) which involve both risks to society and analysis of very long-term consequences possibly extending over many human generations. This paper examines the philosophical underpinnings of the technique which is a special case of utilitarianism, and compares implications of the technique to those arising from alternative ethical systems in analyzing questions of public safety. Ethical systems which emphasize the good of the whole, such as utilitarianism, are shown to differ sharply in decision outcomes from those which emphasize the rights of the individual, such as libertarianism. It is suggested that benefit-cost analysis should be broadened to include alternative weightings of benefits and costs consistent with a variety of ethical views.     

Risk‐Based Decision Making for Reoccupation of Contaminated Areas Following a Wide‐Area Anthrax Release

This article presents an analysis of postattack response strategies to mitigate the risks of reoccupying contaminated areas following a release of Bacillus anthracis spores (the bacterium responsible for causing anthrax) in an urban setting. The analysis is based on a hypothetical attack scenario in which individuals are exposed to B. anthracis spores during an initial aerosol release and then placed on prophylactic antibiotics that successfully protect them against the initial aerosol exposure. The risk from reoccupying buildings contaminated with spores due to their reaerosolization and inhalation is then evaluated. The response options considered include: decontamination of the buildings, vaccination of individuals reoccupying the buildings, extended evacuation of individuals from the contaminated buildings, and combinations of these options. The study uses a decision tree to estimate the costs and benefits of alternative response strategies across a range of exposure risks. Results for best estimates of model inputs suggest that the most cost‐effective response for high‐risk scenarios (individual chance of infection exceeding 11%) consists of evacuation and building decontamination. For infection risks between 4% and 11%, the preferred option is to evacuate for a short period, vaccinate, and then reoccupy once the vaccine has taken effect. For risks between 0.003% and 4%, the preferred option is to vaccinate only. For risks below 0.003%, none of the mitigation actions have positive expected monetary benefits. A sensitivity analysis indicates that for high‐infection‐likelihood scenarios, vaccination is recommended in the case where decontamination efficacy is less than 99.99%.     

Does a Reactor Need a Safety Backfit? Case Study on Communicating Decision and Risk Analysis Information to Managers

An approach to communicating decision and risk analysis findings to managers is illustrated in a real case context. This article consists essentially of a report prepared for senior managers of the Nuclear Regulatory Commission to help them make a reactor safety decision. It illustrates the communication of decision analysis findings relating to technical risks, costs, and benefits in support of a major risk management decision: whether or not to require a safety backfit. Its focus is on the needs of decision makers, and it introduces some novel communication devices.