What Number is “Fifty‐Fifty”?: Redistributing Excessive 50% Responses in Elicited Probabilities

Studies using open-ended response modes to elicit probabilistic beliefs have sometimes found an elevated frequency (or blip) at 50 in their response distributions. Our previous research suggests that this is caused by intrusion of the phrase "fifty-fifty," which represents epistemic uncertainty, rather than a true numeric probability of 50%. Such inappropriate responses pose a problem for decision analysts and others relying on probabilistic judgments. Using an explicit numeric probability scale (ranging from 0-100%) reduces thinking about uncertain events in verbal terms like "fifty-fifty," and, with it, exaggerated use of the 50 response. Here, we present two procedures for adjusting response distributions for data already collected with open-ended response modes and hence vulnerable to an exaggerated presence of 50%. Each procedure infers the prevalence of 50s had a numeric probability scale been used, then redistributes the excess. The two procedures are validated on some of our own existing data and then applied to judgments elicited from experts in groundwater pollution and bioremediation.     

Tornado Warning Perception and Response: Integrating the Roles of Visual Design,Demographics, and Hazard Experience

Recent advancements in severe weather detection and warning dissemination technologies have reduced, but not eliminated, large‐casualty tornado hazards in the United States. Research on warning cognition and behavioral response by the public has the potential to further reduce tornado‐related deaths and injuries; however, less research has been conducted in this area compared to tornado research in the physical sciences. Extant research in this vein tends to bifurcate. One branch of studies derives from classic risk perception, which investigates cognitive, affective, and sociocultural factors in relation to concern and preparation for uncertain risks. Another branch focuses on psychological, social, and cultural factors implicated in warning response for rapid onset hazards, with attention paid to previous experience and message design. Few studies link risk perceptions with cognition and response as elicited by specific examples of warnings. The present study unites risk perception, cognition, and response approaches by testing the contributions of hypothesized warning response drivers in one set of path models. Warning response is approximated by perceived fear and intended protective action as reported by survey respondents when exposed to hypothetical tornado warning scenarios. This study considers the roles of hazard knowledge acquisition, information‐seeking behaviors, previous experience, and sociodemographic factors while controlling for the effects of the visual warning graphic. Findings from the study indicate the primacy of a user's visual interpretation of a warning graphic in shaping tornado warning response. Results also suggest that information‐seeking habits, previous tornado experience, and local disaster culture play strong influencing roles in warning response.     

The Use of Narrative Evidence and Explicit Likelihood by Decisionmakers Varying in Numeracy

Decisionmakers are often presented with explicit likelihood assessments (e.g., there is a 10% chance that an attack will occur over the next three months) and supporting narrative evidence in forecasting and risk communication domains. Decisionmakers are thought to rely on both numerical and narrative information to the extent that they perceive the information to be diagnostic, accurate, and trustworthy. In two studies, we explored how lay decisionmakers varying in numeracy evaluated and used likelihood assessments and narrative evidence in forecasts. Overall, the less numerate reported higher risk and likelihood perceptions. In simple probabilistic forecasts without narrative evidence, decisionmakers at all levels of numeracy were able to use the stated likelihood information, although risk perceptions of the less numerate were more affected by likelihood format. When a forecast includes narrative evidence, decisionmakers were better able to use stated likelihood in a percentage as compared to frequency or verbal formats. The more numerate used stated likelihood more in their evaluations whereas the less numerate focused more on the narrative evidence. These results have important implications for risk analysts and forecasters who need to report the results of their analyses to decisionmakers. Decisionmakers varying in numerical ability may evaluate forecasts in different ways depending on the types of information they find easiest to evaluate.     

Do Temporary Contracts Increase Work Accidents? A Microeconometric Comparison between Italy and Spain

Abstract. This paper analyses the effect of contract type on the rate of work‐related accidents in Italy and Spain, using the 1999 Labour Force Survey ‘ad hoc module’. We find that, once personal and job characteristics of workers are controlled for, the differences in the probability of suffering a work accident between open‐ended and temporary workers vanish. Furthermore, following a novel decomposition analysis by Yun, we obtain that personal and job characteristics tend to increase the probability of having an accident for temporary workers, but the specific influence of contract type favours the latter, who show a lower probability on this account.     

The Impact of Accident Attention,Ideology, and Environmentalism on American Attitudes Toward Nuclear Energy

This study involves the analysis of three waves of survey data about nuclear energy using a probability‐based online panel of respondents in the United States. Survey waves included an initial baseline survey conducted in early 2010, a follow‐up survey conducted in 2010 following the Deepwater Horizon oil spill in the Gulf of Mexico, and an additional follow‐up conducted just after the 2011 Fukushima, Japan, nuclear accident. The central goal is to assess the degree to which changes in public views following an accident are contingent on individual attention and respondent predispositions. Such results would provide real‐world evidence of motivated reasoning. The primary analysis focuses on the impact of Fukushima and how the impact of individual attention to energy issues is moderated by both environmental views and political ideology over time. The analysis uses both mean comparisons and multivariate statistics to test key relationships. Additional variables common in the study of emerging technologies are included in the analysis, including demographics, risk and benefit perceptions, and views about the fairness of decisionmakers in both government and the private sector.     

Bayesian Development of a Dose‐Response Model for Aspergillus fumigatus and Invasive Aspergillosis

Invasive aspergillosis (IA) is a major cause of mortality in immunocompromized hosts, most often consecutive to the inhalation of spores of Aspergillus. However, the relationship between Aspergillus concentration in the air and probability of IA is not quantitatively known. In this study, this relationship was examined in a murine model of IA. Immunosuppressed Balb/c mice were exposed for 60 minutes at day 0 to an aerosol of A. fumigatus spores (Af293 strain). At day 10, IA was assessed in mice by quantitative culture of the lungs and galactomannan dosage. Fifteen separate nebulizations with varying spore concentrations were performed. Rates of IA ranged from 0% to 100% according to spore concentrations. The dose‐response relationship between probability of infection and spore exposure was approximated using the exponential model and the more flexible beta‐Poisson model. Prior distributions of the parameters of the models were proposed then updated with data in a Bayesian framework. Both models yielded close median dose‐responses of the posterior distributions for the main parameter of the model, but with different dispersions, either when the exposure dose was the concentration in the nebulized suspension or was the estimated quantity of spores inhaled by a mouse during the experiment. The median quantity of inhaled spores that infected 50% of mice was estimated at 1.8 × 10⁴ and 3.2 × 10⁴ viable spores in the exponential and beta‐Poisson models, respectively. This study provides dose‐response parameters for quantitative assessment of the relationship between airborne exposure to the reference A. fumigatus strain and probability of IA in immunocompromized hosts.     

A Cognitive‐Affective Scale for Hurricane Risk Perception

The aim of this study was to develop a reliable and valid measure of hurricane risk perception. The utility of such a measure lies in the need to understand how people make decisions when facing an evacuation order. This study included participants located within a 15‐mile buffer of the Gulf and southeast Atlantic U.S. coasts. The study was executed as a three‐wave panel with mail surveys in 2010–2012 (T₀ baseline N = 629, 56%; T₁ retention N = 427, 75%; T₂ retention N = 350, 89%). An inventory based on the psychometric model was developed to discriminate cognitive and affective perceptions of hurricane risk, and included open‐ended responses to solicit additional concepts in the T₀ survey. Analysis of the T₀ data modified the inventory and this revised item set was fielded at T₁ and then replicated at T₂. The resulting scales were assessed for validity against existing measures for perception of hurricane risk, dispositional optimism, and locus of control. A measure of evacuation expectation was also examined as a dependent variable, which was significantly predicted by the new measures. The resulting scale was found to be reliable, stable, and largely valid against the comparison measures. Despite limitations involving sample size, bias, and the strength of some reliabilities, it was concluded that the measure has potential to inform approaches to hurricane preparedness efforts and advance planning for evacuation messages, and that the measure has good promise to generalize to other contexts in natural hazards as well as other domains of risk.     

Methodology for Determining the Appropriateness of a Linear Dose‐Response Function

Microbial food safety risk assessment models can often at times be simplified by eliminating the need to integrate a complex dose‐response relationship across a distribution of exposure doses. This is possible if exposure pathways lead to pathogens at exposure that consistently have a small probability of causing illness. In this situation, the probability of illness will follow an approximately linear function of dose. Consequently, the predicted probability of illness per serving across all exposures is linear with respect to the expected value of dose. The majority of dose‐response functions are approximately linear when the dose is low. Nevertheless, what constitutes “low” is dependent on the parameters of the dose‐response function for a particular pathogen. In this study, a method is proposed to determine an upper bound of the exposure distribution for which the use of a linear dose‐response function is acceptable. If this upper bound is substantially larger than the expected value of exposure doses, then a linear approximation for probability of illness is reasonable. If conditions are appropriate for using the linear dose‐response approximation, for example, the expected value for exposure doses is two to three logs₁₀ smaller than the upper bound of the linear portion of the dose‐response function, then predicting the risk‐reducing effectiveness of a proposed policy is trivial. Simple examples illustrate how this approximation can be used to inform policy decisions and improve an analyst's understanding of risk.     

Probability Elicitation Under Severe Time Pressure: A Rank‐Based Method

Probability elicitation protocols are used to assess and incorporate subjective probabilities in risk and decision analysis. While most of these protocols use methods that have focused on the precision of the elicited probabilities, the speed of the elicitation process has often been neglected. However, speed is also important, particularly when experts need to examine a large number of events on a recurrent basis. Furthermore, most existing elicitation methods are numerical in nature, but there are various reasons why an expert would refuse to give such precise ratio‐scale estimates, even if highly numerate. This may occur, for instance, when there is lack of sufficient hard evidence, when assessing very uncertain events (such as emergent threats), or when dealing with politicized topics (such as terrorism or disease outbreaks). In this article, we adopt an ordinal ranking approach from multicriteria decision analysis to provide a fast and nonnumerical probability elicitation process. Probabilities are subsequently approximated from the ranking by an algorithm based on the principle of maximum entropy, a rule compatible with the ordinal information provided by the expert. The method can elicit probabilities for a wide range of different event types, including new ways of eliciting probabilities for stochastically independent events and low‐probability events. We use a Monte Carlo simulation to test the accuracy of the approximated probabilities and try the method in practice, applying it to a real‐world risk analysis recently conducted for DEFRA (the U.K. Department for the Environment, Farming and Rural Affairs): the prioritization of animal health threats.     

Diminishing Willingness to Pay per Quality‐Adjusted Life Year: Valuing Acute Foodborne Illness

We design and conduct a stated‐preference survey to estimate willingness to pay (WTP) to reduce foodborne risk of acute illness and to test whether WTP is proportional to the corresponding gain in expected quality‐adjusted life years (QALYs). If QALYs measure utility for health, then economic theory requires WTP to be nearly proportional to changes in both health quality and duration of illness and WTP could be estimated by multiplying the expected change in QALYs by an appropriate monetary value. WTP is elicited using double‐bounded, dichotomous‐choice questions in which respondents (randomly selected from the U.S. general adult population, n = 2,858) decide whether to purchase a more expensive food to reduce the risk of foodborne illness. Health risks vary by baseline probability of illness, reduction in probability, duration and severity of illness, and conditional probability of mortality. The expected gain in QALYs is calculated using respondent‐assessed decrements in health‐related quality of life if ill combined with the duration of illness and reduction in probability specified in the survey. We find sharply diminishing marginal WTP for severity and duration of illness prevented. Our results suggest that individuals do not have a constant rate of WTP per QALY, which implies that WTP cannot be accurately estimated by multiplying the change in QALYs by an appropriate monetary value.     

Vulnerability,Risk Perception,and Health Profile of Marginalized People Exposed to Multiple Built‐Environment Stressors in Worcester,Massachusetts: A Pilot Project

Millions of low‐income people of diverse ethnicities inhabit stressful old urban industrial neighborhoods. Yet we know little about the health impacts of built‐environment stressors and risk perceptions in such settings; we lack even basic health profiles. Difficult access is one reason (it took us 30 months to survey 80 households); the lack of multifaceted survey tools is another. We designed and implemented a pilot vulnerability assessment tool in Worcester, Massachusetts. We answer: (1) How can we assess vulnerability to multiple stressors? (2) What is the nature of complex vulnerability—including risk perceptions and health profiles? (3) How can findings be used by our wider community, and what lessons did we learn? (4) What implications arise for science and policy? We sought a holistic picture of neighborhood life. A reasonably representative sample of 80 respondents captured data for 254 people about: demographics, community concerns and resources, time‐activity patterns, health information, risk/stress perceptions, and resources/capacities for coping. Our key findings derive partly from the survey data and partly from our experience in obtaining those data. Data strongly suggest complex vulnerability dominated by psychosocial stress. Unexpected significant gender and ethnic disease disparities emerged: notably, females have twice the disease burden of males, and white females twice the burden of females of color (p < 0.01). Self‐reported depression differentiated by gender and age is illustrative. Community based participatory research (CBPR) approaches require active engagement with marginalized populations, including representatives as funded partners. Complex vulnerability necessitates holistic, participatory approaches to improve scientific understanding and societal responses.     

EXTERNALITIES IN PROGRAM EVALUATION: THE IMPACT OF A WOMEN'S EMPOWERMENT PROGRAM ON IMMUNIZATION

Impact evaluations of development programs usually do not explicitly take into account externalities on non‐participants. Based on a unique dataset we estimate the direct as well as the spillover effects of Mahila Samakhya, a women's empowerment program in India, on child immunization. The survey covers both participants and non‐participants in program villages, as well as respondents in control villages. We account for participation selection bias using instrumental variables. We propose a direct test for the exclusion restriction using the control villages. We find both direct effects on the immunization rates of participants’ children and significant spillovers on immunization rates of non‐participants’ children. The impact of interventions might be substantially underestimated if such external effects were not taken into account. Alternative estimation methods such as propensity score matching and a regression discontinuity approach yield comparable results.     

The Measurement of Subjective Probability: Evaluating the Sensitivity and Accuracy of Various Scales

The RISK of an event generally relates to its expected severity and the perceived probability of its occurrence. In RISK research, however, there is no standard measure for subjective probability estimates. In this study, we compared five commonly used measurement formats—two rating scales, a visual analog scale, and two numeric measures—in terms of their ability to assess subjective probability judgments when objective probabilities are available. We varied the probabilities (low vs. moderate) and severity (low vs. high) of the events to be judged as well as the presentation mode of objective probabilities (sequential presentation of singular events vs. graphical presentation of aggregated information). We employed two complementary goodness‐of‐fit criteria: the correlation between objective and subjective probabilities (sensitivity), and the root mean square deviations of subjective probabilities from objective values (accuracy). The numeric formats generally outperformed all other measures. The severity of events had no effect on the performance. Generally, a rise in probability led to decreases in performance. This effect, however, depended on how the objective probabilities were encoded: pictographs ensured perfect information, which improved goodness of fit for all formats and diminished this negative effect on the performance. Differences in performance between scales are thus caused only in part by characteristics of the scales themselves—they also depend on the process of encoding. Consequently, researchers should take the source of probability information into account before selecting a measure.     

Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis

In this article, we propose an integrated direct and indirect flood risk model for small‐ and large‐scale flood events, allowing for dynamic modeling of total economic losses from a flood event to a full economic recovery. A novel approach is taken that translates direct losses of both capital and labor into production losses using the Cobb‐Douglas production function, aiming at improved consistency in loss accounting. The recovery of the economy is modeled using a hybrid input‐output model and applied to the port region of Rotterdam, using six different flood events (1/10 up to 1/10,000). This procedure allows gaining a better insight regarding the consequences of both high‐ and low‐probability floods. The results show that in terms of expected annual damage, direct losses remain more substantial relative to the indirect losses (approximately 50% larger), but for low‐probability events the indirect losses outweigh the direct losses. Furthermore, we explored parameter uncertainty using a global sensitivity analysis, and varied critical assumptions in the modeling framework related to, among others, flood duration and labor recovery, using a scenario approach. Our findings have two important implications for disaster modelers and practitioners. First, high‐probability events are qualitatively different from low‐probability events in terms of the scale of damages and full recovery period. Second, there are substantial differences in parameter influence between high‐probability and low‐probability flood modeling. These findings suggest that a detailed approach is required when assessing the flood risk for a specific region.     

A Social‐Cognitive Model of Pandemic Influenza H1N1 Risk Perception and Recommended Behaviors in Italy

The outbreak of the pandemic influenza H1N1 2009 (swine flu) between March and April 2009 challenged the health services around the world. Indeed, misconceptions and worries have led the public to refuse to comply with precautionary measures. Moreover, there have been limited efforts to develop models incorporating cognitive, social‐contextual, and affective factors as predictors of compliance with recommended behaviors. The aim of this study was to apply a social‐cognitive model of risk perception and individual response to pandemic influenza H1N1 in a representative sample of Italian population. A sample of 1,010 Italians of at least 18 years of age took part in a telephone survey. The survey included measures of perceived preparedness of institutions, family members and friends’ levels of worry, exposure to media campaigns (social‐contextual factors), perceived coping efficacy, likelihood of infection, perceived seriousness, personal impact, and severity of illness (cognitive evaluations), affective response and compliance with recommended behaviors. Results demonstrated that affective response fully mediated the relationship between cognitive evaluations and social‐contextual factors (with the exception of exposure to media campaigns) and compliance with recommended behaviors. Perceived coping efficacy and preparedness of institutions were not related to compliance with recommended behaviors.     

The Effects of the Recession on Attitudes toward Business Ethics: An Inter‐temporal Study of Business Students in 2001, 2009, and 2010

This inter‐temporal study compares the ethical attitudes of business students at three points: in 2001, when the economy was relatively healthy; in 2009, near the beginning of the current recession; and in 2010, when the economy was worse. Ethical attitudes were measured by replicating a popular survey consisting of 25 ethically charged vignettes. The survey measures willingness to engage in white‐collar crime behaviors, some clearly illegal; others marginally unethical. Findings show an increase in tolerance for clearly illegal behaviors from 2001 to 2009 and an increase in tolerance for an even broader array of unethical acts from 2009 to 2010. As compared with 2001 respondents, 2009 respondents were more tolerant of seriously illegal behaviors but less tolerant of marginally unethical ones. In 2010, respondents were more accepting of both kinds of unethical behaviors. The results also suggest that the change in ethical attitudes from 2009 to 2010 was larger than the average annual change in ethics from 2001 to 2009. These findings are discussed within the framework of classical strain theory, Agnew's General Strain Theory, and the legitimacy of social institutions.     

How Safe Is Safe Enough for Self‐Driving Vehicles?

Self‐driving vehicles (SDVs) promise to considerably reduce traffic crashes. One pressing concern facing the public, automakers, and governments is “How safe is safe enough for SDVs?” To answer this question, a new expressed‐preference approach was proposed for the first time to determine the socially acceptable risk of SDVs. In our between‐subject survey (N = 499), we determined the respondents’ risk‐acceptance rate of scenarios with varying traffic‐risk frequencies to examine the logarithmic relationships between the traffic‐risk frequency and risk‐acceptance rate. Logarithmic regression models of SDVs were compared to those of human‐driven vehicles (HDVs); the results showed that SDVs were required to be safer than HDVs. Given the same traffic‐risk‐acceptance rates for SDVs and HDVs, their associated acceptable risk frequencies of SDVs and HDVs were predicted and compared. Two risk‐acceptance criteria emerged: the tolerable risk criterion, which indicates that SDVs should be four to five times as safe as HDVs, and the broadly acceptable risk criterion, which suggests that half of the respondents hoped that the traffic risk of SDVs would be two orders of magnitude lower than the current estimated traffic risk. The approach and these results could provide insights for government regulatory authorities for establishing clear safety requirements for SDVs.     

Parenthood and Worrying About Climate Change: The Limitations of Previous Approaches

The present study considers the correlation between parenthood and worry about the consequences of climate change. Two approaches to gauging people's perceptions of the risks of climate change are compared: the classic approach, which measures risk perception, and the emotion‐based approach, which measures feelings toward a risk object. The empirical material is based on a questionnaire‐based survey of 3,529 people in Sweden, of whom 1,376 answered, giving a response rate of 39%. The results show that the correlation of parenthood and climate risk is significant when the emotional aspect is raised, but not when respondents were asked to do cognitive estimates of risk. Parenthood proves significant in all three questions that measure feelings, demonstrating that it is a determinant that serves to increase worry about climate change.     

A gradient Markov chain Monte Carlo algorithm for computing multivariate maximum likelihood estimates and posterior distributions: mixture dose-response assessment

Multivariate probability distributions, such as may be used for mixture dose‐response assessment, are typically highly parameterized and difficult to fit to available data. However, such distributions may be useful in analyzing the large electronic data sets becoming available, such as dose‐response biomarker and genetic information. In this article, a new two‐stage computational approach is introduced for estimating multivariate distributions and addressing parameter uncertainty. The proposed first stage comprises a gradient Markov chain Monte Carlo (GMCMC) technique to find Bayesian posterior mode estimates (PMEs) of parameters, equivalent to maximum likelihood estimates (MLEs) in the absence of subjective information. In the second stage, these estimates are used to initialize a Markov chain Monte Carlo (MCMC) simulation, replacing the conventional burn‐in period to allow convergent simulation of the full joint Bayesian posterior distribution and the corresponding unconditional multivariate distribution (not conditional on uncertain parameter values). When the distribution of parameter uncertainty is such a Bayesian posterior, the unconditional distribution is termed predictive. The method is demonstrated by finding conditional and unconditional versions of the recently proposed emergent dose‐response function (DRF). Results are shown for the five‐parameter common‐mode and seven‐parameter dissimilar‐mode models, based on published data for eight benzene–toluene dose pairs. The common mode conditional DRF is obtained with a 21‐fold reduction in data requirement versus MCMC. Example common‐mode unconditional DRFs are then found using synthetic data, showing a 71% reduction in required data. The approach is further demonstrated for a PCB 126‐PCB 153 mixture. Applicability is analyzed and discussed. Matlab^® computer programs are provided.     

Now What Do People Know About Global Climate Change? Survey Studies of Educated Laypeople

In 1992, a mental‐models‐based survey in Pittsburgh, Pennsylvania, revealed that educated laypeople often conflated global climate change and stratospheric ozone depletion, and appeared relatively unaware of the role of anthropogenic carbon dioxide emissions in global warming. This study compares those survey results with 2009 data from a sample of similarly well‐educated laypeople responding to the same survey instrument. Not surprisingly, following a decade of explosive attention to climate change in politics and in the mainstream media, survey respondents in 2009 showed higher awareness and comprehension of some climate change causes. Most notably, unlike those in 1992, 2009 respondents rarely mentioned ozone depletion as a cause of global warming. They were also far more likely to correctly volunteer energy use as a major cause of climate change; many in 2009 also cited natural processes and historical climatic cycles as key causes. When asked how to address the problem of climate change, while respondents in 1992 were unable to differentiate between general “good environmental practices” and actions specific to addressing climate change, respondents in 2009 have begun to appreciate the differences. Despite this, many individuals in 2009 still had incorrect beliefs about climate change, and still did not appear to fully appreciate key facts such as that global warming is primarily due to increased concentrations of carbon dioxide in the atmosphere, and the single most important source of this carbon dioxide is the combustion of fossil fuels.