Elicitation of Expert Judgments of Uncertainty in the Risk Assessment of Herbicide-Tolerant Oilseed Crops

One of the lay public's concerns about genetically modified (GM) organisms (GMO) and related emerging technologies is that not all the important risks are evaluated or even identified yet--and that ignorance of the unanticipated risks could lead to severe environmental or public health consequences. To some degree, even the scientists who participated in the analysis of the risks from GMOs (arguably the people most qualified to critique these analyses) share some of this concern. To formally explore the uncertainty in the risk assessment of a GM crop, we conducted detailed interviews of seven leading experts on GM oilseed crops to obtain qualitative and quantitative information on their understanding of the uncertainties associated with the risks to agriculture from GM oilseed crops (canola or rapeseed). The results of these elicitations revealed three issues of potential concern that are currently left outside the scope of risk assessments. These are (1) the potential loss of the agronomic and environmental benefits of glyphosate (a herbicide widely used in no-till agriculture) due to the combined problems of glyphosate-tolerant canola and wheat volunteer plants, (2) the growing problem of seed lot contamination, and (3) the potential market impacts. The elicitations also identified two areas where knowledge is insufficient. These are: the occurrence of hybridization between canola and wild relatives and the ability of the hybrids to perpetuate themselves in nature, and the fate of the herbicide-tolerance genes in soil and their interaction with soil microfauna and -flora. The methodological contribution of this work is a formal approach to analyzing the uncertainty surrounding complex problems.     

Effects of Knowledge on Attitude Formation and Change Toward Genetically Modified Foods

In three waves, this study investigates the impact of risk and benefit knowledge on attitude formation toward genetically modified (GM) foods as well as the moderating effect of knowledge level on attitude change caused by receiving information. The data in Wave 1 (N = 561) demonstrate that both benefit and risk knowledge either directly contribute to attitude formation or indirectly affect attitudes through the mediating roles of benefit and risk perceptions. Overall, benefit and risk knowledge affect consumer attitudes positively and negatively, respectively. In Wave 2, 486 participants from Wave 1 were provided with information about GM foods, and their attitudes were assessed. Three weeks later, 433 of these participants again reported their attitudes. The results indicate that compared with the benefit and mixed information, risk information has a greater and longer lasting impact on attitude change, which results in lower acceptance of GM foods. Furthermore, risk information more strongly influences participants with a higher knowledge level. The moderating effect of knowledge on attitude change may result from these participants’ better understanding of and greater trust in the information. These findings highlight the important role of knowledge in attitude formation and attitude change toward GM foods as well as the necessity of considering the determinants of attitude formation in attitude change studies.     

An Emerging New Risk Analysis Science: Foundations and Implications

To solve real‐life problems—such as those related to technology, health, security, or climate change—and make suitable decisions, risk is nearly always a main issue. Different types of sciences are often supporting the work, for example, statistics, natural sciences, and social sciences. Risk analysis approaches and methods are also commonly used, but risk analysis is not broadly accepted as a science in itself. A key problem is the lack of explanatory power and large uncertainties when assessing risk. This article presents an emerging new risk analysis science based on novel ideas and theories on risk analysis developed in recent years by the risk analysis community. It builds on a fundamental change in thinking, from the search for accurate predictions and risk estimates, to knowledge generation related to concepts, theories, frameworks, approaches, principles, methods, and models to understand, assess, characterize, communicate, and (in a broad sense) manage risk. Examples are used to illustrate the importance of this distinct/separate risk analysis science for solving risk problems, supporting science in general and other disciplines in particular.     

The Media and Genetically Modified Foods: Evidence in Support of Social Amplification of Risk

Empirical examinations of the "social amplification of risk" framework are rare, partly because of the difficulties in predicting when conditions likely to result in amplification effects will occur. This means that it is difficult to examine changes in risk perception that are contemporaneous with increases and/or decreases in social or media discussion of the risks associated with a particular risk event. However, the collection of attitude data before, during, and after the increased reporting of the risks of genetically modified food in the United Kingdom (spring 1999) has demonstrated that people's risk perceptions do increase and decrease in line with what might be expected upon examination of the amplification and attenuation mechanisms integral to the framework. Perceptions of benefit, however, appeared to be permanently depressed by negative reporting about genetically modified food. Trust in regulatory institutions with responsibility for protecting the public was not affected. It was concluded that the social amplification of risk framework is a useful framework for beginning to explain the potential impact on risk perceptions of a risk event, particularly if that risk event is presented to the public as a new hazard occurring in a crisis context.     

Residential Radon in Canada: An Uncertainty Analysis of Population and Individual Lung Cancer Risk

Following a comprehensive evaluation of the health risks of radon, the U.S. National Research Council (US-NRC) concluded that the radon inside the homes of U.S. residents is an important cause of lung cancer. To assess lung cancer risks associated with radon exposure in Canadian homes, we apply the new (US-NRC) techniques, tailoring assumptions to the Canadian context. A two-dimensional uncertainty analysis is used to provide both population-based (population attributable risk, PAR; excess lifetime risk ratio, ELRR; and life-years lost, LYL) and individual-based (ELRR and LYL) estimates. Our primary results obtained for the Canadian population reveal mean estimates for ELRR, PAR, and LYL are 0.08, 8%, and 0.10 years, respectively. Results are also available and stratified by smoking status (ever versus never). Conveniently, the three indices (ELRR, PAR, and LYL) reveal similar output uncertainty (geometric standard deviation, GSD approximately 1.3), and in the case of ELRR and LYL, comparable variability and uncertainty combined (GSD approximately 4.2). Simplifying relationships are identified between ELRR, LYL, PAR, and the age-specific excess rate ratio (ERR), which suggest a way to scale results from one population to another. This insight is applied in scaling our baseline results to obtain gender-specific estimates, as well as in simplifying and illuminating sensitivity analysis.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

Evaluation of the Quality of Safety and Risk Analysis in the Chemical Industry

Safety and risk analyses have often been criticized as incomplete and inaccurate. This criticism, however, lacks a sound basis, since there has been little scientific evaluation of the methods developed for safety and risk analysis. The aim of this paper is to present a proposal for establishing measures—reliability, validity, and coverage—to be used in the evaluation of the scientific quality of the hazard identification and accident modeling phases in safety and risk analysis. The paper also presents four main principles for the evaluation of the scientific quality, and a theoretical framework to be used in evaluation. The framework is illustrated with practical examples taken from the chemical industry.     

Probabilistic Risk Analyses: NRC Programs and Perspectives

The historical basis and more recent activities and products of probabilistic risk analysis (PRA) in the Atomic Energy Commission and Nuclear Regulatory Commission (NRC) are reviewed. Current NRC program activities and objectives are described. The author's opinions on the best uses of PRA are presented.     

Multiattribute Risk Analysis in Nuclear Emergency Management

Radiation protection authorities have seen a potential for applying multiattribute risk analysis in nuclear emergency management and planning to deal with conflicting objectives, different parties involved, and uncertainties. This type of approach is expected to help in the following areas: to ensure that all relevant attributes are considered in decision making; to enhance communication between the concerned parties, including the public; and to provide a method for explicitly including risk analysis in the process. A multiattribute utility theory analysis was used to select a strategy for protecting the population after a simulated nuclear accident. The value-focused approach and the use of a neutral facilitator were identified as being useful.     

Explaining Public Resistance to Genetically Modified Corn: An Analysis of the Distribution of Benefits and Risks

Genetically modified (GM) crops have met with widespread approval among scientists and policy makers in the United States, but public approval of GM crops, both domestically and abroad, is progressing much more slowly. An underlying cause of public wariness may be that both nations and individual consumers do not perceive significant benefits to themselves from GM crops, while fearing the risks they may incur. In this study, an economic analysis is conducted to determine whether the benefits of one type of GM corn, Bt corn (genetically modified to resist damage from the ECB and Southwestern corn borer), outweigh the potential risks; and who the "winners" and "losers" are among stakeholder groups that may be affected by Bt corn. It is found that Bt corn growers, consumers, and industry all benefit from Bt corn adoption, though the purported health and environmental benefits of reducing chemical pesticide usage through Bt corn are negligible. Though the aggregated public benefit is large, the welfare gain to individual consumers is small and may not make up for perceived risks. While environmental and health risks of Bt corn are unlikely, the potential market risks-impacting both the organic corn market and total U.S. corn exports-are found to be significant. Currently, distributional analysis is not a part of regulatory decision making of Bt corn in the United States; yet it may help to explain why decision makers at both the government and individual-consumer levels have failed to embrace Bt corn and other GM crops.     

企业知识管理绩效的模糊评价模型与分析矩阵

在总结国内外知识管理绩效评价指标体系研究现状的基础上,提出应从过程和结果2个方面对知识管理绩效进行评价;建立了一套实用性较强的知识管理评价指标体系,并尝试用"模糊多级综合评价方法"对企业的知识管理绩效进行评价;指出了对评价得分进行分析的方法,并独创了过程-结果分析矩阵.     

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.     

基于知识贡献考核和效用的组织个体知识共享博弈分析与仿真

本文综合考虑了组织个体在组织知识管理策略——知识贡献绩效考核影响下的个体知识共享行为,建立个体在管理策略动态变化下的共享行为模型,将个体知识共享演化的变化抽象为一个博弈过程,分析纳什均衡存在,构建组织个体知识共享效用函数,综合分析在组织知识贡献考核及个体效用双重条件下,个体知识共享博弈后不会出现“搭便车”现象.在此基础上,进行仿真模拟,探讨组织知识共享与组织激励投入、激励程度、激励差异化及个体效用差异的影响规律.本研究将为组织知识管理策略的制定和控制提供理论依据和指导.     

A Collaborative View of Knowledge in a Knowledge Society: An International Perspective

This paper focuses on key topics that rose to the fore at the `Getting the Story Right' Joint Forum Assembly Meeting between business and higher education leaders of Australia, Canada, Japan, Poland, and the United States which was held in October 1998 in Banff, Canada. Arising from participant observation notes, conversations, document analysis, and reflective writing, the work centers on four major themes: the nature of knowledge and knowledge workers, the fundamental difference between training and learning, the short-term effects of shifting workplace and economic situations, and the long-term impact of intergenerational influences. Each of these knowledge-related themes emerged in collaborative discussions that transcended national, occupational, and cultural boundaries. The paper takes the position that dialogue regarding the nature and usefulness of knowledge paradoxically takes an inward view as it develops outwardly to the case of others and the world. In a collaborative process of this nature, an important dimension of shared human meaning making becomes briefly revealed.     

Uncertainties in Nuclear Probabilistic Risk Analyses

There are many uncertainties in a probabilistic risk analysis (PRA). We identify the different types of uncertainties and describe their implications. We then summarize the uncertainty analyses which have performed in current PRAs and characterize results which have been obtained. We draw conclusions regarding interpretations of uncertainties, areas having largest uncertainties, and needs which exist in uncertainty analysis. We finally characterize the robustness of various utilizations of PRA results.     

Using Probabilistic Terrorism Risk Modeling for Regulatory Benefit-Cost Analysis: Application to the Western Hemisphere Travel Initiative in the Land Environment

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI‐L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk‐reducing effectiveness of WHTI‐L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI‐L, and a range of casualty cost estimates based on the willingness‐to‐pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI‐L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14–26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5–6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit‐cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.     

The Role of Decision Analysis in Risk Analysis: A Retrospective

In commemorating the 40th anniversary of Risk Analysis, this article takes a retrospective look at some of the ways in which decision analysis (as a “sibling field”) has contributed to the development both of the journal, and of risk analysis as a field. I begin with some early foundational papers from the first decade of the journal's history. I then review a number of papers that have applied decision analysis to risk problems over the years, including applications of related methods such as influence diagrams, multicriteria decision analysis, and risk matrices. The article then reviews some recent trends, from roughly the last five years, and concludes with observations about the parallel evolution of risk analysis and decision analysis over the decades—especially with regard to the importance of representing multiple stakeholder perspectives, and the importance of behavioral realism in decision models. Overall, the extensive literature surveyed here supports the view that the incorporation of decision-analytic perspectives has improved the practice of risk analysis.     

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal, this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors’ accomplishments, and proposes 10 major risk-related challenges for 2020–2030. Authors conclude that the next decade will severely test the field of risk analysis.     

Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.     

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.