Specifying Risk Goals: Inherent Problems with Democratic Institutions

Many risks have the property that large numbers of people are exposed and have little or no individual control over the risks they face. Dams, nuclear power plants, and recombinant DNA research have proven controversial not simply because there is vast uncertainty about the true level of risk associated with each, but because a fundamental issue is that in each case social risks can be lessened by spending more on safety: dams can be designed to withstand larger earthquakes, nuclear plants can have additional safety equipment, and DNA research could be done in yet more carefully isolated laboratories. Since each person will have preferences regarding the proper trade-off between increased cost and increased safety, there is little possibility of consensus. More importantly, we show that a voting process for expressing individual preferences can be manipulated and is seriously flawed in the sense that it does not lead to an "efficient" outcome. In addition, we show that virtually all people are unhappy with the safety decision, in the sense that each would prefer either a safer or a cheaper outcome. Thus, making safety decisions that affect a large group of people who will not be able to control the outcome is even more difficult than has been appreciated. We suggest some ways of handling some of the difficulties.     

An Evaluation of the Treatment of Risk and Uncertainties in the IPCC Reports on Climate Change

Few global threats rival global climate change in scale and potential consequence. The principal international authority assessing climate risk is the Intergovernmental Panel on Climate Change (IPCC). Through repeated assessments the IPCC has devoted considerable effort and interdisciplinary competence to articulating a common characterization of climate risk and uncertainties. We have reviewed the assessment and its foundation for the Fifth Assessment Reports published in 2013 and 2014, in particular the guidance note for lead authors of the fifth IPCC assessment report on consistent treatment of uncertainties. Our analysis shows that the work carried out by the ICPP is short of providing a theoretically and conceptually convincing foundation on the treatment of risk and uncertainties. The main reasons for our assessment are: (i) the concept of risk is given a too narrow definition (a function of consequences and probability/likelihood); and (ii) the reports lack precision in delineating their concepts and methods. The goal of this article is to contribute to improving the handling of uncertainty and risk in future IPCC studies, thereby obtaining a more theoretically substantiated characterization as well as enhanced scientific quality for risk analysis in this area. Several suggestions for how to improve the risk and uncertainty treatment are provided.     

Crisis Management: Informing a New Leadership Research Agenda

As the business community becomes more complex, crisis events are likely to increase in both prevalence and severity. Whether management scholarship has kept pace with this new reality is debatable. Moreover, much of the existing crisis research—perhaps understandably—stems from a negative frame: crises are threats or problems to be overcome. Such research has produced relevant insight into crisis handling, has helped categorize the plethora of crisis events, and has connected crisis events to relevant management strategies. We argue here that this framing fundamentally limits the types of questions asked and the methodological approaches used to answer those questions. Perhaps worse, given the important role that leadership plays in crisis handling, this negative frame can hinder the possibilities for the practice and study of leadership. In this article, we review an array of crisis research and explore two theoretical domains—issue framing and deviance—and their potential role for influencing leadership theory. We discuss the challenges of conducting crisis research, and offer suggestions for new methodological approaches and new research questions that are consistent with a more positive leadership approach.     

Technical Note—Demand Uncertainty Reduction in Decentralized Supply Chains

This note analyzes the effects associated with reducing demand uncertainty in a decentralized supply chain comprising one manufacturer, one retailer, and a wholesale price contract that governs the transactions between them. The demand uncertainty level is parameterized through a mean‐preserving spread, and the manufacturer's and the retailer's equilibrium decisions are solved accordingly. We consider the case of an exogenous retail price as well as the case of an endogenous retail price, and we find in both cases that the manufacturer's and the retailer's expected profits in equilibrium are not necessarily monotone decreasing in the uncertainty level. Thus, we find that, even if the cost of reducing demand uncertainty is zero, uncertainty reduction can hurt rather than benefit either or both members of the supply chain.     

Consumer-Phase Salmonella enterica serovar Enteritidis Risk Assessment for Egg-Containing Food Products

We describe a one-dimensional probabilistic model of the role of domestic food handling behaviors on salmonellosis risk associated with the consumption of eggs and egg-containing foods. Six categories of egg-containing foods were defined based on the amount of egg contained in the food, whether eggs are pooled, and the degree of cooking practiced by consumers. We used bootstrap simulation to quantify uncertainty in risk estimates due to sampling error, and sensitivity analysis to identify key sources of variability and uncertainty in the model. Because of typical model characteristics such as nonlinearity, interaction between inputs, thresholds, and saturation points, Sobol's method, a novel sensitivity analysis approach, was used to identify key sources of variability. Based on the mean probability of illness, examples of foods from the food categories ranked from most to least risk of illness were: (1) home-made salad dressings/ice cream; (2) fried eggs/boiled eggs; (3) omelettes; and (4) baked foods/breads. For food categories that may include uncooked eggs (e.g., home-made salad dressings/ice cream), consumer handling conditions such as storage time and temperature after food preparation were the key sources of variability. In contrast, for food categories associated with undercooked eggs (e.g., fried/soft-boiled eggs), the initial level of Salmonella contamination and the log10 reduction due to cooking were the key sources of variability. Important sources of uncertainty varied with both the risk percentile and the food category under consideration. This work adds to previous risk assessments focused on egg production and storage practices, and provides a science-based approach to inform consumer risk communications regarding safe egg handling practices.     

企业动态能力及其功效:环境不确定性的影响

当前关于动态能力的维度及其功效还存在较大分歧,有些学者甚至认为动态能力毫无价值,应该摒弃动态能力研究.本文从战略过程视角,依据相关文献与访谈调查,将动态能力分解为组织意会能力、柔性决策能力及动态执行能力,探讨了动态能力与持续优势的关系及环境不确定性在其中的影响作用.通过对中国地区217家企业的实证研究发现,动态能力确实对持续优势有显著的正向影响,环境不确定性是动态能力的驱动因素而非调节变量,环境动态性对企业持续优势有显著的正向影响,敌对性则对持续优势有显著的负向影响.本研究的结论澄清了动态能力的价值前提,探明了环境不确定性的影响机理,对理论研究与实践操作都有一定的启发意义.     

Integrating a Top‐Down and a Bottom‐Up Approach: Formal and Informal Risk‐Handling Strategies in a Utility Company

In this study, we bring together a top‐down and a bottom‐up approach of risk handling. We do so by conceptualizing and qualitatively and quantitatively measuring formal and informal risk‐handling strategies in a Dutch utility company. We conceive of formal risk handling as regulating, training, and educating safety and enforcing rule compliance, while we distinguish three different informal risk‐handling strategies: discretionary specialization, tacit knowledge, and taking personal responsibility. We show that the formal risk‐handling strategy and the three informal risk‐handling strategies can be measured separately. Hence, we have validated the measurement of all four strategies derived from two different risk‐handling approaches. Moreover, we have demonstrated that the perceived use of the four strategies has different effects on unsafe behavior: formal risk handling and tacit knowledge decrease it, discretion increases it, and taking personal responsibility has no effect on unsafe behavior.     

The Role of Quantitative Risk Assessments for Characterizing Risk and Uncertainty and Delineating Appropriate Risk Management Options, with Special Emphasis on Terrorism Risk

The purpose of this article is to discuss the role of quantitative risk assessments for characterizing risk and uncertainty and delineating appropriate risk management options. Our main concern is situations (risk problems) with large potential consequences, large uncertainties, and/or ambiguities (related to the relevance, meaning, and implications of the decision basis; or related to the values to be protected and the priorities to be made), in particular terrorism risk. We look into the scientific basis of the quantitative risk assessments and the boundaries of the assessments in such a context. Based on a risk perspective that defines risk as uncertainty about and severity of the consequences (or outcomes) of an activity with respect to something that humans value we advocate a broad risk assessment approach characterizing uncertainties beyond probabilities and expected values. Key features of this approach are qualitative uncertainty assessment and scenario building instruments.     

Quantitative Uncertainty Analysis for a Weed Risk Assessment System

Weed risk assessments (WRA) are used to identify plant invaders before introduction. Unfortunately, very few incorporate uncertainty ratings or evaluate the effects of uncertainty, a fundamental risk component. We developed a probabilistic model to quantitatively evaluate the effects of uncertainty on the outcomes of a question‐based WRA tool for the United States. In our tool, the uncertainty of each response is rated as Negligible, Low, Moderate, or High. We developed the model by specifying the likelihood of a response changing for each uncertainty rating. The simulations determine if responses change, select new responses, and sum the scores to determine the risk rating. The simulated scores reveal potential variation in WRA risk ratings. In testing with 204 species assessments, the ranges of simulated risk scores increased with greater uncertainty, and analyses for most species produced simulated risk ratings that differed from the baseline WRA rating. Still, the most frequent simulated rating matched the baseline rating for every High Risk species, and for 87% of all tested species. The remaining 13% primarily involved ambiguous Low Risk results. Changing final ratings based on the uncertainty analysis results was not justified here because accuracy (match between WRA tool and known risk rating) did not improve. Detailed analyses of three species assessments indicate that assessment uncertainty may be best reduced by obtaining evidence for unanswered questions, rather than obtaining additional evidence for questions with responses. This analysis represents an advance in interpreting WRA results, and has enhanced our regulation and management of potential weed species.     

Estimating Conditional Probabilities of Terrorist Attacks: Modeling Adversaries with Uncertain Value Tradeoffs

Many analyses conducted to inform security decisions depend on estimates of the conditional probabilities of different attack alternatives. These probabilities are difficult to estimate since analysts have limited access to the adversary and limited knowledge of the adversary’s utility function, so subject matter experts often provide the estimates through direct elicitation. In this article, we describe a method of using uncertainty in utility function value tradeoffs to model the adversary’s decision process and solve for the conditional probabilities of different attacks in closed form. The conditional probabilities are suitable to be used as inputs to probabilistic risk assessments and other decision support techniques. The process we describe is an extension of value‐focused thinking and is broadly applicable, including in general business decision making. We demonstrate the use of this technique with simple examples.     

Sensitivity Analysis of a Two-Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.     

Perceived Risk and Uncertainty of Nuclear Waste: Differences Among Science,Business, and Environmental Group Members1

This paper presents survey results on perceived risk regarding the management of nuclear waste. Using data taken from random mail surveys of members of scientific, business, and environmental groups in Colorado and New Mexico in the summer of 1990, we examine differences between the groups in their expressed perceptions of risk and also their assessments of the certainty of their beliefs. We consider whether (a) greater uncertainty is associated with greater perceived risks and (b) whether there is greater responsiveness to new information for those whose beliefs are least certain. We find that there are connections between perceived risk and uncertainty, and there is a greater tendency to update risk assessements from a position of greater initial uncertainty. There are, however, differences between the groups as well as asymmetries in their responses to new information. The latter suggests that perceived risks will ratchet upward over time even when information is not biased toward more or less risk.     

A Bayesian Semiparametric Model for Radiation Dose‐Response Estimation

In evaluating the risk of exposure to health hazards, characterizing the dose‐response relationship and estimating acceptable exposure levels are the primary goals. In analyses of health risks associated with exposure to ionizing radiation, while there is a clear agreement that moderate to high radiation doses cause harmful effects in humans, little has been known about the possible biological effects at low doses, for example, below 0.1 Gy, which is the dose range relevant to most radiation exposures of concern today. A conventional approach to radiation dose‐response estimation based on simple parametric forms, such as the linear nonthreshold model, can be misleading in evaluating the risk and, in particular, its uncertainty at low doses. As an alternative approach, we consider a Bayesian semiparametric model that has a connected piece‐wise‐linear dose‐response function with prior distributions having an autoregressive structure among the random slope coefficients defined over closely spaced dose categories. With a simulation study and application to analysis of cancer incidence data among Japanese atomic bomb survivors, we show that this approach can produce smooth and flexible dose‐response estimation while reasonably handling the risk uncertainty at low doses and elsewhere. With relatively few assumptions and modeling options to be made by the analyst, the method can be particularly useful in assessing risks associated with low‐dose radiation exposures.     

The Impact of Portfolio Location Uncertainty on Probabilistic Seismic Risk Analysis

Probabilistic seismic risk analysis is a well‐established method in the insurance industry for modeling portfolio losses from earthquake events. In this context, precise exposure locations are often unknown. However, so far, location uncertainty has not been in the focus of a large amount of research. In this article, we propose a novel framework for treatment of location uncertainty. As a case study, a large number of synthetic portfolios resembling typical real‐world cases were created. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on the variability of loss frequency estimations. The results indicate that due to loss aggregation effects and spatial hazard variability, location uncertainty in isolation and in conjunction with ground motion uncertainty can induce significant variability to probabilistic loss results, especially for portfolios with a small number of risks. After quantifying its effect, we conclude that location uncertainty should not be neglected when assessing probabilistic seismic risk, but should be treated stochastically and the resulting variability should be visualized and interpreted carefully.     

Safety Stock,Excess Capacity or Diversification: Trade‐Offs under Supply and Demand Uncertainty

Firms mitigate uncertainty in demand and supply by carrying safety stock, planning for excess capacity and diversifying supply sources. In this study, we provide a framework to jointly optimize these three levers in a periodic review infinite horizon setting, and in particular we examine how one can reduce inventory and capacity investments through proper diversification strategies. Observing that a modified base‐stock inventory policy is optimal, we find that the capacity‐diversification problem is well behaved and characterize the optimal mix of safety stock, excess capacity and extra number of supply sources. We find that higher supply uncertainty results in higher safety stock, more excess capacity, and higher diversification. But safety stock and diversification are non‐monotonic in demand uncertainty. Our results can be extended to situations in which suppliers are heterogeneous, and can be used to develop effective heuristics.     

Parametric Distributions for Soil Ingestion by Children

This note presents parameterized distributions of estimates of the amount of soil ingested by children based on data collected by Binder et al. (1986). Following discussions with Dr. Binder, we modified the Binder study data by using the actual stool weights instead of the 15 g value used in the original study. After testing the data for lognormality, we generated parameterized distributions for use in risk assessment uncertainty analyses such as Monte Carlo simulations.     

Uncertainties in the Link Between Global Climate Change and Predicted Health Risks from Pollution: Hexachlorobenzene (HCB) Case Study Using a Fugacity Model

Industrial societies have altered the earth's environment in ways that could have important, long-term ecological, economic, and health implications. In this paper, we examine the extent to which uncertainty about global climate change could impact the precision of predictions of secondary outcomes such as health impacts of pollution. Using a model that links global climate change with predictions of chemical exposure and human health risk in the Western region of the United States of America (U.S.), we define parameter variabilities and uncertainties and we characterize the resulting outcome variance. As a case study, we consider the public health consequences from releases of hexachlorobenzene (HCB), a ubiquitous multimedia pollutant. By constructing a matrix that links global environmental change both directly and indirectly to potential human-health effects attributable to HCB released into air, soil, and water, we define critical parameter variances in the health risk estimation process. We employ a combined uncertainty/sensitivity analysis to investigate how HCB releases are affected by increasing atmospheric temperature and the accompanying climate alterations that are anticipated. We examine how such uncertainty impacts both the expected magnitude and calculational precision of potential human exposures and health effects. This assessment reveals that uncertain temperature increases of up to 5°C have little impact on either the magnitude or precision of the public-health consequences estimated under existing climate variations for HCB released into air and water in the Western region of the U.S.     

Informing and Educating the Public About Risk

The objective of informing and educating the public about risk issues seems easy to attain in principle, but, in practice, may be difficult to accomplish. This paper attempts to illustrate why this is so. To be effective, risk communicators must recognize and overcome a number of obstacles that have their roots in the limitations of scientific risk assessment and the idiosyncrasies of the human mind. Doing an adequate job of communicating means finding comprehensible ways of presenting complex technical material that is clouded by uncertainty and inherently difficult to understand. The problems may not be insurmountable, however, if designers of risk information programs are sensitive to the difficulties.     

Regulating Under Uncertainty: Newsboy for Exposure Limits

Setting action levels or limits for health protection is complicated by uncertainty in the dose-response relation across a range of hazards and exposures. To address this issue, we consider the classic newsboy problem. The principles used to manage uncertainty for that case are applied to two stylized exposure examples, one for high dose and high dose rate radiation and the other for ammonia. Both incorporate expert judgment on uncertainty quantification in the dose-response relationship. The mathematical technique of probabilistic inversion also plays a key role. We propose a coupled approach, whereby scientists quantify the dose-response uncertainty using techniques such as structured expert judgment with performance weights and probabilistic inversion, and stakeholders quantify associated loss rates.     

Seeing What You Want to See: How Imprecise Uncertainty Ranges Enhance Motivated Reasoning

In this article, we consider a novel criterion for evaluating representations of uncertainty ranges, namely, the extent to which a representation enhances motivated reasoning. In two studies, we show that perceptions of the distribution underlying ambiguous numerical ranges are affected by the motivations and worldviews of end users. This motivated reasoning effect remained after controlling for objective numeracy and fluid intelligence but was attenuated when the correct interpretation was made clear. We suggest that analysts and communicators explicitly consider the potential for motivated evaluation when evaluating uncertainty displays.