管理者风险态度对系统能力受损后的应急采购决策影响研究

通信、电力和石化等大型运作系统核心能力受损后的应急期间内,管理者并非遵循风险规避的准则进行决策,为降低应急管理的成本,管理者是否应该采取激进或者保守的态度？为验证该观点,本文在文献[3]研究的基础上进行了拓展,在伙伴企业应急期间提供快速和正常两种能力支援的情境下,将管理者风险态度引入能力应急采购的决策问题。通过理论证明和数值仿真,本文得到了3个有益的应急管理结论:管理者的风险态度并不是越激进越好,"极端事件下采取双源供应"并非最优选择,应急期间管理者应首要考虑获得伙伴企业的能力恢复技术支援。     

Modeling Microbial Growth Within Food Safety Risk Assessments

Risk estimates for food-borne infection will usually depend heavily on numbers of microorganisms present on the food at the time of consumption. As these data are seldom available directly, attention has turned to predictive microbiology as a means of inferring exposure at consumption. Codex guidelines recommend that microbiological risk assessment should explicitly consider the dynamics of microbiological growth, survival, and death in foods. This article describes predictive models and resources for modeling microbial growth in foods, and their utility and limitations in food safety risk assessment. We also aim to identify tools, data, and knowledge sources, and to provide an understanding of the microbial ecology of foods so that users can recognize model limits, avoid modeling unrealistic scenarios, and thus be able to appreciate the levels of confidence they can have in the outputs of predictive microbiology models. The microbial ecology of foods is complex. Developing reliable risk assessments involving microbial growth in foods will require the skills of both microbial ecologists and mathematical modelers. Simplifying assumptions will need to be made, but because of the potential for apparently small errors in growth rate to translate into very large errors in the estimate of risk, the validity of those assumptions should be carefully assessed. Quantitative estimates of absolute microbial risk within narrow confidence intervals do not yet appear to be possible. Nevertheless, the expression of microbial ecology knowledge in "predictive microbiology" models does allow decision support using the tools of risk assessment.     

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.     

Secondary Risk Theory: Validation of a Novel Model of Protection Motivation

Protection motivation theory states individuals conduct threat and coping appraisals when deciding how to respond to perceived risks. However, that model does not adequately explain today's risk culture, where engaging in recommended behaviors may create a separate set of real or perceived secondary risks. We argue for and then demonstrate the need for a new model accounting for a secondary threat appraisal, which we call secondary risk theory. In an online experiment, 1,246 participants indicated their intention to take a vaccine after reading about the likelihood and severity of side effects. We manipulated likelihood and severity in a 2 × 2 between‐subjects design and examined how well secondary risk theory predicts vaccination intention compared to protection motivation theory. Protection motivation theory performed better when the likelihood and severity of side effects were both low (R² = 0.30) versus high (R² = 0.15). In contrast, secondary risk theory performed similarly when the likelihood and severity of side effects were both low (R² = 0.42) or high (R² = 0.45). But the latter figure is a large improvement over protection motivation theory, suggesting the usefulness of secondary risk theory when individuals perceive a high secondary threat.     

Perception of Climate Risk among Rural Farmers in Vietnam: Consistency within Households and with the Empirical Record

Rural farmers in Vietnamese communes perceive climate risk and potential impacts on livelihood within a complex context that may influence individual and household decisions. In a primary survey of 1,145 residents of the Thach Ha district of Ha Tinh province, we gathered data regarding perception about stability in climate, potential risks to livelihood, demographic characteristics, orientation toward risk, and interest in expanding economic activity. Temporal analysis of meteorological and economic indicator data forms an empirical basis for comparison with human perception. We ask the basic question: Are rural farmers’ perceptions of climate consistent with the historical record and reproducible within households? We find that respondents do perceive climate anomalies, with some anchoring on recent extreme events as revealed by climate observational data, and further that spouses disproportionately share perceptions relative to randomly simulated pairings. To put climate‐related risk perception in a larger context, we examine patterns across a range of risks to livelihood faced by farmers (livestock disease, pests, markets, health), using dimension reduction techniques. We find that our respondents distinguish among potential causes of low economic productivity, with substantial emphasis on climate‐related impacts. They do not express uniform concern across risks, but rather average patterns reveal common modes and distinguish climate concern. Still, among those expressing concern about climate‐related risks to livelihood we do not find an association with expressed intention to pursue changes in economic activity as a risk management response.     

Test-Retest Reliability of User Information Satisfaction: A Comment on Galletta and Lederer's Paper

Galletta and Lederer [10] concluded that the short-form measure of user information satisfaction (UIS) lacks sufficient test-retest reliability. We question the validity of this conclusion since the method used by the authors for assessing test-retest reliability is not grounded in classical reliability theory. Analysis of their data suggests that the UIS instrument has adequate test-retest reliability.     

Exploring the "Psychometric Paradigm": Comparisons Between Aggregate and Individual Analyses

The "psychometric paradigm" developed by Slovic, Fischhoff, and Lichtenstein was a landmark in research about public attitudes toward risks. One problem with this work, however, was that (at least initially) it did not attempt to distinguish between individuals or groups of people, except "experts" vs. "lay people." This paradigm produced a "cognitive map" of hazards, and the assumption seemed to be that the characteristics identified were inherent attributes of risk. This paper examines the validity of this assumption. A questionnaire survey similar to those designed by Slovic et al. was conducted, but the data were analyzed at both the aggregate level, using mean scores, and at the level of individuals ( N = 131 Norwich residents). The results reported here demonstrate that (1) individuals vary in their perception of the same risk issue; (2) individuals vary in their rating of the same risk characteristics on the same risk issue; and (3) some of the strong intercorrelations observed between risk characteristics at the aggregate level are not supported when the same data are analysed at the level of individuals. Despite these findings, the relationship between risk characteristics and risk perceptions inferred by the psychometric paradigm did hold true at the level of individuals, for most—but not all—of the characteristics. In particular, the relationship between "lack of knowledge to those exposed" and risk perceptions appears to be a complex one, a finding which has important implications for risk communication strategies.     

Testing the Validity of the Sibling Sex Ratio Instrument

We test the validity of the sibling sex ratio instrument suggested by Angrist and Evans using the methods proposed by Kitagawa and Huber and Mellace. The sex ratio of the first two siblings is arguably randomly assigned and influences the probability of having a third child, which makes it a candidate instrument for fertility when estimating the effect of fertility on female labor supply. However, identification hinges on the random assignment of the instrument, an instrumental exclusion restriction, and the monotonicity of fertility in the instrument. We find that the instrumental variable tests of Kitagawa and Huber and Mellace do not point to a violation of these assumptions in the Angrist and Evans data (which can, however, not be ruled out even asymptotically as the tests cannot detect all possible violations).     

A Survey of Approaches for Assessing and Managing the Risk of Extremes

In this paper, we review methods for assessing and managing the risk of extreme events, where extreme events are defined to be rare, severe, and outside the normal range of experience of the system in question. First, we discuss several systematic approaches for identifying possible extreme events. We then discuss some issues related to risk assessment of extreme events, including what type of output is needed (e.g., a single probability vs. a probability distribution), and alternatives to the probabilistic approach. Next, we present a number of probabilistic methods. These include: guidelines for eliciting informative probability distributions from experts; maximum entropy distributions; extreme value theory; other approaches for constructing prior distributions (such as reference or noninformative priors); the use of modeling and decomposition to estimate the probability (or distribution) of interest; and bounding methods. Finally, we briefly discuss several approaches for managing the risk of extreme events, and conclude with recommendations and directions for future research.     

Understanding the Ecological Validity of Relying Practice as a Basis for Risk Identification

Understanding the reliability of hazardous organizations and their protective systems is central to understanding the risk they produce. Work on “high reliability organization” has done much to illuminate the conditions in which social organization becomes reliable in highly demanding conditions. But risk depends just as much on how relying entities do their relying as it does on the reliability of the entities they rely on. Patterns of relying are often opaque in sociotechnical systems, and processes of relying and being relied on are mutually influencing in complex ways, so the relationship between relying and risk may not be at all obvious. This study was an attempt to study relying as a social practice, in particular analyzing how it had ecological validity in a social organization—how practice was responsive to the conditions in which it took place. This involved observational fieldwork and inductive, qualitative analysis on an offshore oil and gas production platform that was nearing the end of its design life and undergoing refurbishment. The analysis produced four main categories of ecological validity: responsiveness to formal organization, responsiveness to situational contingency, responsiveness to information asymmetry, and responsiveness to sociomateriality. This ecological validity of relying practice should be a primary focus of risk identification, assessing how relying can become mismatched to reliability in certain ways, both when relying practice is responsive to circumstances and when it is not.     

Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment

Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.     

Learning strategies of workers in the knowledge-creating company

This study presents a critical examination of Nonaka and Takeuchi's theory about knowledge-creating companies (1995), taken as one example of contemporary management theories concerning innovation and learning. Two main questions are investigated. First, how is the learning of workers organized in the knowledge creation theory? Second, how is their learning related to the work they perform? Answers to these questions are sought from case-study research into work-related learning projects (Poell 1998) and from theoretical notions about learning networks in various work types (Van der Krogt 1998). Our first conclusion is that Nonaka and Takeuchi seem to assume that workers will learn only within the boundaries set by management. They do not, however, take into account that workers organize a great deal of learning themselves, frequently irrespective of management expectations. Second, Nonaka and Takeuchi expect workers to learn according to rigid bureaucratic principles in a work context emphasizing innovation. The theory of knowledge creation does not show how these contrary principles might be successfully integrated.     

The Constrained Extremal Distribution Selection Method

Engineering design and policy formulation often involve the assessment of the likelihood of future events commonly expressed through a probability distribution. Determination of these distributions is based, when possible, on observational data. Unfortunately, these data are often incomplete, biased, and/or incorrect. These problems are exacerbated when policy formulation involves the risk of extreme events—situations of low likelihood and high consequences. Usually, observational data simply do not exist for such events. Therefore, determination of probabilities which characterize extreme events must utilize all available knowledge, be it subjective or observational, so as to most accurately reflect the likelihood of such events. Extending previous work on the statistics of extremes, the Constrained Extremal Distribution Selection Method is a methodology that assists in the selection of probability distributions that characterize the risk of extreme events using expert opinion to constrain the feasible values for parameters which explicitly define a distribution. An extremal distribution is then "fit" to observational data, conditional that the selection of parameters does not violate any constraints. Using a random search technique, genetic algorithms, parameters that minimize a measure of fit between a hypothesized distribution and observational data are estimated. The Constrained Extremal Distribution Selection Method is applied to a real world policy problem faced by the U.S. Environmental Protection Agency. Selected distributions characterize the likelihood of extreme, fatal hazardous material accidents in the United States. These distributions are used to characterize the risk of large scale accidents with numerous fatalities.     

What Risks Do People Perceive in Everyday Life? A Perspective Gained from the Experience Sampling Method (ESM)

The experience sampling method (ESM) was used to collect data from 74 part-time students who described and assessed the risks involved in their current activities when interrupted at random moments by text messages. The major categories of perceived risk were short term in nature and involved "loss of time or materials" related to work and "physical damage" (e.g., from transportation). Using techniques of multilevel analysis, we demonstrate effects of gender, emotional state, and types of risk on assessments of risk. Specifically, females do not differ from males in assessing the potential severity of risks but they see these as more likely to occur. Also, participants assessed risks to be lower when in more positive self-reported emotional states. We further demonstrate the potential of ESM by showing that risk assessments associated with current actions exceed those made retrospectively. We conclude by noting advantages and disadvantages of ESM for collecting data about risk perceptions.     

Life-Cycle Assessment in Relation to Risk Assessment: An Evolving Perspective

A life-cycle approach takes a cradle-to-grave perspective of a product's numerous activities from the raw material extraction to final disposal. There have been recent efforts to develop life-cycle assessment (LCA) to assess both environmental and human health issues. The question then arises: what are the capabilities of LCA, especially in relation to risk assessment? To address this question, this paper first describes the LCA mass-based accounting system and then analyzes the use of this approach for environmental and human health assessment. The key LCA limitations in this respect are loss of spatial, temporal, dose-response, and threshold information. These limitations affect LCA's capability to assess several environmental issues, and human health in particular. This leads to the conclusion that LCA impact assessment does not predict or measure actual effects, quantitate risks, or address safety. Instead, LCA uses mass loadings with simplifying assumptions and subjective judgments to add independent effects and exposures into an overall score. As a result, LCA identifies possible human health issues on a systemwide basis from a worst case, hypothetical hazard perspective. Ideally, the identified issues would then be addressed by more detailed assessment methods, such as risk assessment.     

Communicating Actionable Risk for Terrorism and Other Hazards⋆

We propose a shift in emphasis when communicating to people when the objective is to motivate household disaster preparedness actions. This shift is to emphasize the communication of preparedness actions (what to do about risk) rather than risk itself. We have called this perspective “communicating actionable risk,” and it is grounded in diffusion of innovations and communication theories. A representative sample of households in the nation was analyzed using a path analytic framework. Preparedness information variables (including content, density, and observation), preparedness mediating variables (knowledge, perceived effectiveness, and milling), and preparedness actions taken were modeled. Clear results emerged that provide a strong basis for communicating actionable risk, and for the conclusion both that information observed (seeing preparedness actions that other have taken) and information received (receiving recommendations about what preparedness actions to take) play key, although different, roles in motivating preparedness actions among the people in our nation.     

An Adjustment Factor for Mode-of-Action Uncertainty with Dual-Mode Carcinogens: The Case of Naphthalene-Induced Nasal Tumors in Rats

The U.S. Environmental Protection Agency (USEPA) guidelines for cancer risk assessment recognize that some chemical carcinogens may have a site-specific mode of action (MOA) involving mutation and cell-killing-induced hyperplasia. The guidelines recommend that for such dual MOA (DMOA) carcinogens, judgment should be used to compare and assess results using separate "linear" (genotoxic) versus "nonlinear" (nongenotoxic) approaches to low-level risk extrapolation. Because the guidelines allow this only when evidence supports reliable risk extrapolation using a validated mechanistic model, they effectively prevent addressing MOA uncertainty when data do not fully validate such a model but otherwise clearly support a DMOA. An adjustment-factor approach is proposed to address this gap, analogous to reference-dose procedures used for classic toxicity endpoints. By this method, even when a "nonlinear" toxicokinetic model cannot be fully validated, the effect of DMOA uncertainty on low-dose risk can be addressed. Application of the proposed approach was illustrated for the case of risk extrapolation from bioassay data on rat nasal tumors induced by chronic lifetime exposure to naphthalene. Bioassay data, toxicokinetic data, and pharmacokinetic analyses were determined to indicate that naphthalene is almost certainly a DMOA carcinogen. Plausibility bounds on rat-tumor-type-specific DMOA-related uncertainty were obtained using a mechanistic two-stage cancer risk model adapted to reflect the empirical link between genotoxic and cytotoxic effects of the most potent identified genotoxic naphthalene metabolites, 1,2- and 1,4-naphthoquinone. Bound-specific adjustment factors were then used to reduce naphthalene risk estimated by linear extrapolation (under the default genotoxic MOA assumption), to account for the DMOA exhibited by this compound.     

Experimental Evidence Against the Paradigm of Mortality Risk Aversion

This article deals with the question of how societal impacts of fatal accidents can be integrated into the management of natural or man‐made hazards. Today, many governmental agencies give additional weight to the number of potential fatalities in their risk assessments to reflect society's aversion to large accidents. Although mortality risk aversion has been proposed in numerous risk management guidelines, there has been no evidence that lay people want public decisionmakers to overweight infrequent accidents of large societal consequences against more frequent ones of smaller societal consequences. Furthermore, it is not known whether public decisionmakers actually do such overweighting when they decide upon the mitigation of natural or technical hazards. In this article, we report on two experimental tasks that required participants to evaluate negative prospects involving 1–100 potential fatalities. Our results show that neither lay people nor hazard experts exhibit risk‐averse behavior in decisions on mortality risks.     

A New Approach to Hazardous Materials Transportation Risk Analysis: Decision Modeling to Identify Critical Variables

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity , and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.