Some Limitations of Qualitative Risk Rating Systems

Qualitative systems for rating animal antimicrobial risks using ordered categorical labels such as “high,”“medium,” and “low” can potentially simplify risk assessment input requirements used to inform risk management decisions. But do they improve decisions? This article compares the results of qualitative and quantitative risk assessment systems and establishes some theoretical limitations on the extent to which they are compatible. In general, qualitative risk rating systems satisfying conditions found in real‐world rating systems and guidance documents and proposed as reasonable make two types of errors: (1) Reversed rankings, i.e., assigning higher qualitative risk ratings to situations that have lower quantitative risks; and (2) Uninformative ratings, e.g., frequently assigning the most severe qualitative risk label (such as “high”) to situations with arbitrarily small quantitative risks and assigning the same ratings to risks that differ by many orders of magnitude. Therefore, despite their appealing consensus‐building properties, flexibility, and appearance of thoughtful process in input requirements, qualitative rating systems as currently proposed often do not provide sufficient information to discriminate accurately between quantitatively small and quantitatively large risks. The value of information (VOI) that they provide for improving risk management decisions can be zero if most risks are small but a few are large, since qualitative ratings may then be unable to confidently distinguish the large risks from the small. These limitations suggest that it is important to continue to develop and apply practical quantitative risk assessment methods, since qualitative ones are often unreliable.     

Exploring Risk Judgments in a Trade Dispute Using Bayesian Networks

Bayesian networks (BNs) are graphical modeling tools that are generally recommended for exploring what‐if scenarios, visualizing systems and problems, and for communication between stakeholders during decision making. In this article, we investigate their potential for exploring different perspectives in trade disputes. To do so, we draw on a specific case study that was arbitrated by the World Trade Organization (WTO): the Australia‐New Zealand apples dispute. The dispute centered on disagreement about judgments contained within Australia's 2006 import risk analysis (IRA). We built a range of BNs of increasing complexity that modeled various approaches to undertaking IRAs, from the basic qualitative and semi‐quantitative risk analyses routinely performed in government agencies, to the more complex quantitative simulation undertaken by Australia in the apples dispute. We found the BNs useful for exploring disagreements under uncertainty because they are probabilistic and transparently represent steps in the analysis. Different scenarios and evidence can easily be entered. Specifically, we explore the sensitivity of the risk output to different judgments (particularly volume of trade). Thus, we explore how BNs could usefully aid WTO dispute settlement. We conclude that BNs are preferable to basic qualitative and semi‐quantitative risk analyses because they offer an accessible interface and are mathematically sound. However, most current BN modeling tools are limited compared with complex simulations, as was used in the 2006 apples IRA. Although complex simulations may be more accurate, they are a black box for stakeholders. BNs have the potential to be a transparent aid to complex decision making, but they are currently computationally limited. Recent technological software developments are promising.     

Designing Graphs that Promote Both Risk Understanding and Behavior Change

Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk‐avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm (“foreground+background”) versus only those affected (“foreground‐only”), and the presence versus absence of simple numerical labels above bars. Foreground‐only displays were associated with larger risk perceptions and risk‐avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground‐only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground‐only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk‐avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk‐avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground‐only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.     

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。     

Overcoming barriers to integrating economic analysis into risk assessment

Regulatory risk analysis is designed to provide decisionmakers with a clearer understanding of how policies are likely to affect risk. The systems that produce risk are biological, physical, and social and economic. As a result, risk analysis is an inherently interdisciplinary task. Yet in practice, risk analysis has been interdisciplinary in only limited ways. Risk analysis could provide more accurate assessments of risk if there were better integration of economics and other social sciences into risk assessment itself. This essay examines how discussions about risk analysis policy have influenced the roles of various disciplines in risk analysis. It explores ways in which integrated bio/physical-economic modeling could contribute to more accurate assessments of risk. It reviews examples of the kind of integrated economics-bio/physical modeling that could be used to enhance risk assessment. The essay ends with a discussion of institutional barriers to greater integration of economic modeling into risk assessment and provides suggestions on how these might be overcome.     

Consumer Risk Perceptions Toward Agricultural Biotechnology, Self-Protection, and Food Demand: The Case of Milk in the United States

This study is an econometric systems approach to modeling the factors and linkages affecting risk perceptions toward agricultural biotechnology, self-protection actions, and food demand. This model is applied to milk in the United States, but it can be adapted to other products as well as other categories of risk perceptions. The contribution of this formulation is the ability to examine how explanatory factors influence risk perceptions and whether they translate into behavior and ultimately what impact this has on aggregate markets. Hadden's outrage factors on heightening risk perceptions are among the factors examined. In particular, the article examines the role of labeling as a means of permitting informed consent to mitigate outrage factors. The effects of attitudinal, economic, and demographic factors on risk perceptions are also explored, as well as the linkage between risk perceptions, consumer behavior, and food demand. Because risk perceptions and self-protection actions are categorical variables and demand is a continuous variable, the model is estimated as a two-stage mixed system with a covariance correction procedure suggested by Amemiya. The findings indicate that it is the availability of labeling, not the price difference, between that labeled milk and milk produced with recombinant bovine Somatotropin (rbST) that significantly affects consumer's selection of rbST-free milk. The results indicate that greater availability of labeled milk would not only significantly increase the proportion of consumers who purchased labeled milk, its availability would also reduce the perception of risk associated with rbST, whether consumers purchase it or not. In other words, availability of rbST-free milk translates into lower risk perceptions toward milk produced with rbST.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

European Public Perceptions of Food Risk: Cross-National and Methodological Comparisons

This article presents a comparative study of public perceptions of food risk across 25 European member states. A secondary data analysis is conducted on a Eurobarometer survey fielded to nationally representative samples in 2005. The survey included closed questions as well as free associations to map risk perceptions. Taking a quantitative approach, we find that people in a majority of European countries express similar levels of concern about food risks. However, outside this majority a North-South divide is evident, with the Northern countries worrying less than the Southern countries. Multilevel modeling shows that cross-national differences in individual respondents' level of worry are in part attributable to shared country effects and to generalized risk sensitivity about a range of personal risks. On the underlying structure of food risk concerns, factor analysis points to three dimensions described by groupings of risks related to adulteration and contamination, health effects, and production and hygiene. A qualitative analysis of respondents' free associations about problems and risks with food identifies three major themes that are consistent with the quantitative results. However, the free associations also point toward greater cross-national diversity and to striking variations in the range and importance of food risks. Overall, the picture is of a public that frames food risks in a wider context of beliefs about the links between diet and health. We conclude with some implications for research on food risk perceptions in particular and risk perception studies in general.     

Comparison of the Cancer Risk of Methylene Chloride Predicted from Animal Bioassay Data with the Epidemiologic Evidence

Methylene chloride has been shown to be a lung and liver carcinogen in the mouse; yet, the current epidemiologic data show no adverse health effects associated with chronic exposure to this compound. Hearne et al. have compared the results of a large mortality study on occupational exposure to methylene chloride to the human risk predictions based on the rodent bioassay to point out the inconsistency between the animal toxicologic and human epidemiologic data. The maximum number of lung and liver cancers predicted due to methylene chloride exposure based on the rodent bioassay data was 24 compared to 14 deaths from these cancers actually observed in the Hearne et al. epidemiology study. We assess the minimum risk detectable by the human study in order to calculate the upperbound potency of methylene chloride and compare it to the potency derived from the bioassay data. Results from the epidemiology study imply an upperbound potency of 1.5 x 10(-2) per ppm, compared to 1.4 x 10(-2) per ppm calculated using the most conservative analysis of the animal data. We conclude that the negative epidemiology study of Hearne et al. is not sufficiently powerful to show that the risk is inconsistent with the human risk estimated by modeling the rodent bioassay data. Specifically, the doses to which the workers were exposed, the population studied, and the latency period were not adequate to determine that the risks are outside the bounds of the risk estimates predicted by low-dose modeling of the animal data.     

Mental Models in Risk Assessment: Informing People About Drugs

One way to communicate about the risks of drugs is through the use of package inserts. The problems associated with this medium of informing patients have been investigated by several researchers who found that people require information about drugs they are using, including extensive risk information, and that they are willing to take this information into account in their usage of drugs. But empirical results also show that people easily misinterpret the information given. A conceptual framework is proposed that might be used for better understanding the cognitive processes involved in such a type of risk assessment and communication. It is based on the idea that people develop, through experience, a mental model of how a drug works, which effects it might produce, that contraindications have to be considered, etc. This mental model is "run" when a specific package insert has been read and a specific question arises such as, for example, whether certain symptoms can be explained as normal or whether they require special attention and action. We argue that the mental model approach offers a useful perspective for examining how people understand package inserts, and consequently for improving their content and design. The approach promises to be equally useful for other aspects of risk analysis that are dependent upon human judgment and decision making, e.g., threat diagnosis and human reliability analysis.     

Tolerable or Acceptable: A Comparison of Risk Regulation in the United Kingdom and in the Netherlands

In managing major accident hazards in industry risk, both the United Kingdom and the Netherlands use quantitative risk analysis and quantified risk criteria. These should be understood in the historical, legal, and political context in which they were drawn up. Even when criteria are numerically the same they do not necessarily mean the same thing. The legal and historical context differs widely between the two countries. Nevertheless, it is surprising that the final results in terms of land use and levels of risk are very similar.     

Government Risk Management Priorities: A Comparison of the Preferences of Asian Indian Americans and Other Americans

A survey was conducted of approximately 200 Asian Indian Americans and 200 other residents of New Jersey in order to understand the risk management priorities that they want government to have. We found that Asian Indian Americans, especially younger women, focused on personal/family risks, such as alcohol and drug abuse, sexual abuse, and domestic violence. The New Jersey comparison group, in contrast, placed war/terrorism and loss of health care services and insurance at the top of their priorities for government. These results suggest stressful acculturation-related issues within the Asian Indian community. Both populations want more risk management from government than they believe government is currently providing. Respondents who wanted more from government tended to dread the risk, be fearful of the consequences, trust government, and have a feeling of personal efficacy. Within the Asian Indian American sample, wide variations were observed by language spoken at home and religious affiliation. Notably, Muslims and Hindi language speakers tended not to trust government and hence wanted less government involvement. This study supports our call for studies of recent migrant populations and Johnson's for testing ethnic identity and acculturation as factors in risk judgments.     

Why Reduced-Form Regression Models of Health Effects Versus Exposures Should Not Replace QRA: Livestock Production and Infant Mortality as an Example

Do pollution emissions from livestock operations increase infant mortality rate (IMR)? A recent regression analysis of changes in IMR against changes in aggregate “animal units” (a weighted sum of cattle, pig, and poultry numbers) over time, for counties throughout the United States, suggested the provocative conclusion that they do: “[A] doubling of production leads to a 7.4% increase in infant mortality.” Yet, we find that regressing IMR changes against changes in specific components of “animal units” (cattle, pigs, and broilers) at the state level reveals statistically significant negative associations between changes in livestock production (especially, cattle production) and changes in IMR. We conclude that statistical associations between livestock variables and IMR variables are very sensitive to modeling choices (e.g., selection of explanatory variables, and use of specific animal types vs. aggregate “animal units). Such associations, whether positive or negative, do not warrant causal interpretation. We suggest that standard methods of quantitative risk assessment (QRA), including emissions release (source) models, fate and transport modeling, exposure assessment, and dose-response modeling, really are important—and indeed, perhaps, essential—for drawing valid causal inferences about health effects of exposures to guide sound, well-informed public health risk management policy. Reduced-form regression models, which skip most or all of these steps, can only quantify statistical associations (which may be due to model specification, variable selection, residual confounding, or other noncausal factors). Sound risk management requires the extra work needed to identify and model valid causal relations.     

Cumulative Response to Sequences of Terror Attacks Varying in Frequency and Trajectory

There is a paucity of research examining public response to the cumulative effects of multiple related extreme events over time. We investigated the separate and combined effects of frequency and trajectory of terrorist attacks. A scenario simulation of a series of gas station bombings in Southern California was developed to evaluate respondents' affect, risk perception, and intended avoidance behavior using a 3 (frequency; low vs. medium vs. high) by 3 (trajectory; increasing vs. constant vs. decreasing) factorial design. For each of the nine conditions, three videos were created to simulate news broadcasts documenting the attacks over a three‐week period. A total of 275 respondents were included in the analysis. Results from analysis of covariances (ANCOVAs) indicate that trajectory of the sequential attacks (increasing or decreasing in frequency) predicts negative affect, risk perception, and avoidance behavior. In contrast, frequency predicts neither negative affect, positive affect, risk perception, nor intended avoidance behavior. Results from structural equation modeling (SEM) further indicate that the effect of negative affect on behavioral intention is mediated by risk perception and the effect of trajectory on risk perception is partially mediated by negative affect. In addition, both ANCOVAs and SEM model results suggest that (1) females experience less positive affect and perceive more risk than males, (2) respondents with higher income perceive more risk, and (3) younger respondents are more likely to modify their behavior to avoid the risk of future attacks.     

Risk Modeling of Interdependent Complex Systems of Systems: Theory and Practice

The emergence of the complexity characterizing our systems of systems (SoS) requires a reevaluation of the way we model, assess, manage, communicate, and analyze the risk thereto. Current models for risk analysis of emergent complex SoS are insufficient because too often they rely on the same risk functions and models used for single systems. These models commonly fail to incorporate the complexity derived from the networks of interdependencies and interconnectedness (I–I) characterizing SoS. There is a need to reevaluate currently practiced risk analysis to respond to this reality by examining, and thus comprehending, what makes emergent SoS complex. The key to evaluating the risk to SoS lies in understanding the genesis of characterizing I–I of systems manifested through shared states and other essential entities within and among the systems that constitute SoS. The term “essential entities” includes shared decisions, resources, functions, policies, decisionmakers, stakeholders, organizational setups, and others. This undertaking can be accomplished by building on state‐space theory, which is fundamental to systems engineering and process control. This article presents a theoretical and analytical framework for modeling the risk to SoS with two case studies performed with the MITRE Corporation and demonstrates the pivotal contributions made by shared states and other essential entities to modeling and analysis of the risk to complex SoS. A third case study highlights the multifarious representations of SoS, which require harmonizing the risk analysis process currently applied to single systems when applied to complex SoS.     

Cases of real-life policies related to risk: How can they enhance risk analysis and risk science?

Policies on risk constitute a core topic of risk analysis and risk science, and it is common at risk conferences to present real-life cases of such policies, for example related to the handling of climate change and pandemics. Although these are of broad interest, showing how important issues in society are dealt with, it can be questioned to what extent and how these cases contribute to enhancing risk analysis and risk science. The present paper addresses this concern. It is argued that, in order to learn from the cases, they need in general to be more thoroughly followed up with discussions of concepts, principles, approaches, and methods for assessing, characterizing, communicating and handling risk. Describing a governmental policy on, for example, the handling of COVID-19 is a point of departure for interesting discussions concerning its justification and performance, in particular in relation to risk and the most updated knowledge from the risk analysis field. Such discussions are, however, often lacking. The paper points to some key obstacles and challenges for the learning process, including the difficulty of distinguishing between policies, policy analysis, and politics.     

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.     

A Comprehensive Network Security Risk Model for Process Control Networks

The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.     

Use of Advances in Technology for Maritime Risk Assessment

The maritime industry is moving toward a "goal-setting" risk-based regime. This opens the way to safety engineers to explore and exploit flexible and advanced risk modeling and decision-making approaches in the design and operation processes. In this article, following a brief review of the current status of maritime risk assessment, a design/operation selection framework and a design/operation optimization framework are outlined. A general discussion of control engineering techniques and their application to risk modeling and decision making is given. Four novel risk modeling and decision-making approaches are then outlined with illustrative examples to demonstrate their use. Such approaches may be used as alternatives to facilitate risk modeling and decision making in situations where conventional techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in general engineering and technology are suggested with respect to risk modeling and decision making.