The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

Self‐driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self‐driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self‐driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self‐driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self‐driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self‐driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self‐driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.     

An Empirical Model of Perceived Mortality Risks for Selected U.S. Arsenic Hot Spots

Researchers have long recognized that subjective perceptions of risk are better predictors of choices over risky outcomes than science‐based or experts’ assessments of risk. More recent work suggests that uncertainty about risks also plays a role in predicting choices and behavior. In this article, we develop and estimate a formal model for an individual's perceived health risks associated with arsenic contamination of his or her drinking water. The modeling approach treats risk as a random variable, with an estimable probability distribution whose variance reflects uncertainty. The model we estimate uses data collected from a survey given to a sample of people living in arsenic‐prone areas in the United States. The findings from this article support the fact that scientific information is essential to explaining the mortality rate perceived by the individuals, but uncertainty about the probability remains significant.     

Expanding the Political Philosophy Dimension of the RISP Model: Examining the Conditional Indirect Effects of Cultural Cognition

This article attempts to connect literatures from the Risk Information Seeking and Processing (RISP) model and cultural cognition theory. We do this by assessing the relationship between the two prominent cultural cognition variables (i.e., group and grid) and risk perceptions. We then examine whether these risk perceptions are associated with three outcomes important to the RISP model: information seeking, systematic processing, and heuristic processing, through a serial mediation model. We used 2015 data collected from 10 communities across the United States to test our hypotheses. Our results show that people high on group and low on grid (egalitarian communitarians) show greater risk perceptions regarding water quality issues. Moreover, these higher levels of perceived risk translate into increased information seeking, systematic processing of information, and lower heuristic processing through intervening variables from the RISP model (e.g., negative emotions and information insufficiency). These results extend the extant literature by expanding on the treatment of political ideology within the RISP model literature and taking a more nuanced approach to political beliefs in accordance with the cultural cognitions literature. Our article also expands on the RISP literature by looking at information‐processing variables.     

QMRA for Drinking Water: 1. Revisiting the Mathematical Structure of Single‐Hit Dose‐Response Models

Dose‐response models are essential to quantitative microbial risk assessment (QMRA), providing a link between levels of human exposure to pathogens and the probability of negative health outcomes. In drinking water studies, the class of semi‐mechanistic models known as single‐hit models, such as the exponential and the exact beta‐Poisson, has seen widespread use. In this work, an attempt is made to carefully develop the general mathematical single‐hit framework while explicitly accounting for variation in (1) host susceptibility and (2) pathogen infectivity. This allows a precise interpretation of the so‐called single‐hit probability and precise identification of a set of statistical independence assumptions that are sufficient to arrive at single‐hit models. Further analysis of the model framework is facilitated by formulating the single‐hit models compactly using probability generating and moment generating functions. Among the more practically relevant conclusions drawn are: (1) for any dose distribution, variation in host susceptibility always reduces the single‐hit risk compared to a constant host susceptibility (assuming equal mean susceptibilities), (2) the model‐consistent representation of complete host immunity is formally demonstrated to be a simple scaling of the response, (3) the model‐consistent expression for the total risk from repeated exposures deviates (gives lower risk) from the conventional expression used in applications, and (4) a model‐consistent expression for the mean per‐exposure dose that produces the correct total risk from repeated exposures is developed.     

Considering the Risk of Infection by Cryptosporidium via Consumption of Municipally Treated Drinking Water from a Surface Water Source in a Southwestern Ontario Community

Through the use of case‐control analyses and quantitative microbial risk assessment (QMRA), relative risks of transmission of cryptosporidiosis have been evaluated (recreational water exposure vs. drinking water consumption) for a Canadian community with higher than national rates of cryptosporidiosis. A QMRA was developed to assess the risk of Cryptosporidium infection through the consumption of municipally treated drinking water. Simulations were based on site‐specific surface water contamination levels and drinking water treatment log₁₀ reduction capacity for Cryptosporidium. Results suggested that the risk of Cryptosporidium infection via drinking water in the study community, assuming routine operation of the water treatment plant, was negligible (6 infections per 10¹³ persons per day—5th percentile: 2 infections per 10¹⁵ persons per day; 95th percentile: 3 infections per 10¹² persons per day). The risk is essentially nonexistent during optimized, routine treatment operations. The study community achieves between 7 and 9 log₁₀Cryptosporidium oocyst reduction through routine water treatment processes. Although these results do not preclude the need for constant vigilance by both water treatment and public health professionals in this community, they suggest that the cause of higher rates of cryptosporidiosis are more likely due to recreational water contact, or perhaps direct animal contact. QMRA can be successfully applied at the community level to identify data gaps, rank relative public health risks, and forecast future risk scenarios. It is most useful when performed in a collaborative way with local stakeholders, from beginning to end of the risk analysis paradigm.     

The Value of a Statistical Life for Risk‐Averse and Risk‐Seeking Individuals

This article estimates the value of a statistical life (VSL) for Chile under the hedonic wage method while accounting for individual risk preferences. Two alternative measures of risk aversion are used. First, risk aversion is directly measured using survey measures of preferences over hypothetical gambles, and second, over observed individual behaviors that may proxy for risk preferences, such as smoking status, are used. I reconcile the results with a theoretical model of economic behavior that predicts how the wage‐risk tradeoff changes as risk aversion differs across individuals. The VSL estimates range between 0.61 and 8.68 million dollars. The results using smoking behavior as a proxy for risk attitudes are consistent with previous findings. However, directly measuring risk aversion corrects the wage‐risk tradeoff estimation bias in the opposite direction. The results are robust to other observed measures of risk aversion such as drinking behavior and stock investments. Results suggest that, consistent with the literature that connects smoking behavior with labor market outcomes, smoking status could be capturing poor health productivity effect in addition to purely risk preferences.     

Evaluating the Potential for a Helicobacter pylori Drinking Water Guideline

Helicobacter pylori is a microaerophilic, gram‐negative bacterium that is linked to adverse health effects including ulcers and gastrointestinal cancers. The goal of this analysis is to develop the necessary inputs for a quantitative microbial risk assessment (QMRA) needed to develop a potential guideline for drinking water at the point of ingestion (e.g., a maximum contaminant level, or MCL) that would be protective of human health to an acceptable level of risk while considering sources of uncertainty. Using infection and gastric cancer as two discrete endpoints, and calculating dose‐response relationships from experimental data on humans and monkeys, we perform both a forward and reverse risk assessment to determine the risk from current reported surface water concentrations of H. pylori and an acceptable concentration of H. pylori at the point of ingestion. This approach represents a synthesis of available information on human exposure to H. pylori via drinking water. A lifetime risk of cancer model suggests that a MCL be set at <1 organism/L given a 5‐log removal treatment because we cannot exclude the possibility that current levels of H. pylori in environmental source waters pose a potential public health risk. Research gaps include pathogen occurrence in source and finished water, treatment removal rates, and determination of H. pylori risks from other water sources such as groundwater and recreational water.     

Risk Perception and Information Processing: The Development and Validation of a Questionnaire to Assess Self‐Reported Information Processing

The role of information processing in understanding people's responses to risk information has recently received substantial attention. One limitation of this research concerns the unavailability of a validated questionnaire of information processing. This article presents two studies in which we describe the development and validation of the Information‐Processing Questionnaire to meet that need. Study 1 describes the development and initial validation of the questionnaire. Participants were randomized to either a systematic processing or a heuristic processing condition after which they completed a manipulation check and the initial 15‐item questionnaire and again two weeks later. The questionnaire was subjected to factor reliability and validity analyses on both measurement times for purposes of cross‐validation of the results. A two‐factor solution was observed representing a systematic processing and a heuristic processing subscale. The resulting scale showed good reliability and validity, with the systematic condition scoring significantly higher on the systematic subscale and the heuristic processing condition significantly higher on the heuristic subscale. Study 2 sought to further validate the questionnaire in a field study. Results of the second study corresponded with those of Study 1 and provided further evidence of the validity of the Information‐Processing Questionnaire. The availability of this information‐processing scale will be a valuable asset for future research and may provide researchers with new research opportunities.     

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.     

Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context

The aim of this study was to develop a modified quantitative microbial risk assessment (QMRA) framework that could be applied as a decision support tool to choose between alternative drinking water interventions in the developing context. The impact of different household water treatment (HWT) interventions on the overall incidence of diarrheal disease and disability adjusted life years (DALYs) was estimated, without relying on source water pathogen concentration as the starting point for the analysis. A framework was developed and a software tool constructed and then implemented for an illustrative case study for Nepal based on published scientific data. Coagulation combined with free chlorine disinfection provided the greatest estimated health gains in the short term; however, when long‐term compliance was incorporated into the calculations, the preferred intervention was porous ceramic filtration. The model demonstrates how the QMRA framework can be used to integrate evidence from different studies to inform management decisions, and in particular to prioritize the next best intervention with respect to estimated reduction in diarrheal incidence. This study only considered HWT interventions; it is recognized that a systematic consideration of sanitation, recreation, and drinking water pathways is important for effective management of waterborne transmission of pathogens, and the approach could be expanded to consider the broader water‐related context.     

Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry

Low‐probability, high‐impact events are difficult to manage. Firms may underinvest in risk assessments for low‐probability, high‐impact events because it is not easy to link the direct and indirect benefits of doing so. Scholarly research on the effectiveness of programs aimed at reducing such events faces the same challenge. In this article, we draw on comprehensive industry‐wide data from the U.S. nuclear power industry to explore the impact of conducting probabilistic risk assessment (PRA) on preventing safety‐related disruptions. We examine this using data from over 25,000 monthly event reports across 101 U.S. nuclear reactors from 1985 to 1998. Using Poisson fixed effects models with time trends, we find that the number of safety‐related disruptions reduced between 8% and 27% per month in periods after operators submitted their PRA in response to the Nuclear Regulatory Commission's Generic Letter 88‐20, which required all operators to conduct a PRA. One possible mechanism for this is that the adoption of PRA may have increased learning rates, lowering the rate of recurring events by 42%. We find that operators that completed their PRA before Generic Letter 88‐20 continued to experience safety improvements during 1990–1995. This suggests that revisiting PRA or conducting it again can be beneficial. Our results suggest that even in a highly safety‐conscious industry as nuclear utilities, a more formal approach to quantifying risk has its benefits.     

Bounding Analysis of Drinking Water Health Risks from a Spill of Hydraulic Fracturing Flowback Water

A bounding risk assessment is presented that evaluates possible human health risk from a hypothetical scenario involving a 10,000‐gallon release of flowback water from horizontal fracturing of Marcellus Shale. The water is assumed to be spilled on the ground, infiltrates into groundwater that is a source of drinking water, and an adult and child located downgradient drink the groundwater. Key uncertainties in estimating risk are given explicit quantitative treatment using Monte Carlo analysis. Chemicals that contribute significantly to estimated health risks are identified, as are key uncertainties and variables to which risk estimates are sensitive. The results show that hypothetical exposure via drinking water impacted by chemicals in Marcellus Shale flowback water, assumed to be spilled onto the ground surface, results in predicted bounds between 10⁻¹⁰ and 10⁻⁶ (for both adult and child receptors) for excess lifetime cancer risk. Cumulative hazard indices (HI_CUMULATIVE) resulting from these hypothetical exposures have predicted bounds (5th to 95th percentile) between 0.02 and 35 for assumed adult receptors and 0.1 and 146 for assumed child receptors. Predicted health risks are dominated by noncancer endpoints related to ingestion of barium and lithium in impacted groundwater. Hazard indices above unity are largely related to exposure to lithium. Salinity taste thresholds are likely to be exceeded before drinking water exposures result in adverse health effects. The findings provide focus for policy discussions concerning flowback water risk management. They also indicate ways to improve the ability to estimate health risks from drinking water impacted by a flowback water spill (i.e., reducing uncertainty).     

How Anxiety Leads to Suboptimal Decisions Under Risky Choice Situations

The current research proposes that situationally activated anxiety—whether incidental or integral—impairs decision making. In particular, we theorize that anxiety drives decisionmakers to more heavily emphasize subjective anecdotal information in their decision making, at the expense of more factual statistical information—a deleterious heuristic called the anecdotal bias. Four studies provide consistent support for this assertion. Studies 1A and 1B feature field experiments that demonstrate the role of incidental anxiety in enhancing the anecdotal bias in a choice context. Study 2 builds on these findings, manipulating individuals’ incidental anxiety and showing how this affects the anecdotal bias in the context of message evaluations. Study 2 also provides direct evidence that only high‐arousal negative emotions such as anxiety/worry enhance the anecdotal bias, not just any negative emotion (e.g., sadness). While the first three studies examine how incidental anxiety impacts choice, the last study demonstrates the effect of integral anxiety on decision making, manipulating anxiety by intensifying participants’ perceived risk. Our results show that—consistent with findings from our first three studies—the anecdotal bias is enhanced when anxiety is heightened by individuals’ perception of risk.     

Disentangling the Influence of Value Predispositions and Risk/Benefit Perceptions on Support for Nanotechnology Among the American Public

Using nanotechnology as a case study, this article explores (1) how people's perceptions of benefits and risks are related to their approval of nanotechnology, (2) which information‐processing factors contribute to public risk/benefit perceptions, and (3) whether individuals’ predispositions (i.e., deference to scientific authority and ideology) may moderate the relationship between cognitive processing and risk perceptions of the technology. Results indicate that benefit perceptions positively affect public support for nanotechnology; perceptions of risk tend to be more influenced by systematic processing than by heuristic cues, whereas both heuristic and systematic processing influence benefit perceptions. People who are more liberal‐minded tend to be more affected by systematic processing when thinking about the benefits of nanotechnology than those who are more conservative. Compared to less deferent individuals, those who are more deferent to scientific authority tend to be less influenced by systematic processing when making judgments about the benefits and risks of nanotechnology. Implications are discussed.     

Protective Measures,Personal Experience,and the Affective Psychology of Time

We examined the role of time and affect in intentions to purchase a risk‐protective measure (Studies 1 and 2) and explored participant abilities to factor time into the likelihood judgments that presumably underlie such intentions (Study 3). Participants worried more about losing their possessions and were more likely to purchase a protective measure given a longer term lease than a short‐term lease, but only if their belongings were described in affect‐poor terms. If described instead as being particularly special and affect‐rich, participants neglected time and were about equally likely to purchase a risk‐protective measure for shorter and longer term leases. However, and consistent with prior literature, the cognitive mechanism underlying this time‐neglect‐with‐affect‐richness effect seemed to be the greater use of the affect heuristic in the shorter term than the longer term. Study 2 results demonstrated that prior experience with having been burglarized amplified the interactive effect of time and affect. Greater deliberation did not attenuate this effect as hypothesized whether deliberation was measured through numeracy or manipulated through instructions. The results of Study 3 indicated that few participants are able to calculate correctly the risk numbers necessary to take time into account. Two possible solutions to encourage more purchases of protective measures in the long term are discussed.     

Nanotechnology,Risk, and Oversight: Learning Lessons from Related Emerging Technologies

Emerging technologies are defined by their novelty and thus are accompanied by significant uncertainty in determining appropriate ways to manage risks associated with them. Yet, there is a body of prior knowledge about risk management and oversight policy for other technologies that have already permeated society. Here, we describe two ways in which prospective oversight policy analysis for emerging technologies can draw upon these past experiences. One involves comparing specific products that have already been marketed to similar products of the emerging technology (cognate‐product approach). The other treats the emerging technology as a body of products and methods and relates it to another technological field that has already emerged and penetrated markets (whole‐technology approach). In this article, we describe our work using these approaches to inform risk and oversight policy for nanotechnology and its products. We draw parallels between biotechnology and nanotechnology as whole fields of development and also between genetically engineered organisms in the food supply and agricultural products of nanotechnology. Through these comparisons, we find that both approaches to historical learning have value and present lessons that could be applied to nanotechnology.     

Treating and Drinking Well Water in the Presence of Health Risks from Arsenic Contamination: Results from a U.S. Hot Spot

The Safe Drinking Water Act of 1974 regulates water quality in public drinking water supply systems but does not pertain to private domestic wells, often found in rural areas throughout the country. The recent decision to tighten the drinking water standard for arsenic from 50 parts per billion (ppb) to 10 ppb may therefore affect some households in rural communities, but may not directly reduce health risks for those on private wells. The article reports results from a survey conducted in a U.S. arsenic hot spot, the rural area of Churchill County, Nevada. This area has elevated levels of arsenic in groundwater. We find that a significant proportion of households on private wells are consuming drinking water with arsenic levels that pose a health risk. The decision to treat tap water for those on private wells in this area is modeled, and the predicted probability of treatment is used to help explain drinking water consumption. This probability represents behaviors relating to the household's perception of risk.     

Quantitative Links Between Arsenic Exposure and Influenza A (H1N1) Infection‐Associated Lung Function Exacerbations Risk

The objective of this study was to link arsenic exposure and influenza A (H1N1) infection‐induced respiratory effects to assess the impact of arsenic‐contaminated drinking water on exacerbation risk of A (H1N1)‐associated lung function. The homogeneous Poisson process was used to approximate the related processes between arsenic exposure and influenza‐associated lung function exacerbation risk. We found that (i) estimated arsenic‐induced forced expiratory volume in 1 second (FEV₁) reducing rates ranged from 0.116 to 0.179 mL/μg for age 15–85 years, (ii) estimated arsenic‐induced A (H1N1) viral load increasing rate was 0.5 mL/μg, (iii) estimated A (H1N1) virus‐induced FEV₁ reducing rate was 0.10 mL/logTCID50, and (iv) the relationship between arsenic exposure and A (H1N1)‐associated respiratory symptoms scores (RSS) can be described by a Hill model. Here we showed that maximum RSS at day 2 postinfection for Taiwan, West Bengal (India), and the United States were estimated to be in the severe range of 0.83, 0.89, and 0.81, respectively, indicating that chronic arsenic exposure and A (H1N1) infection together are most likely to pose potential exacerbations risk of lung function, although a 50% probability of lung function exacerbations risk induced by arsenic and influenza infection was within the mild and moderate ranges of RSS at day 1 and 2 postinfection. We concluded that avoidance of drinking arsenic‐containing water could significantly reduce influenza respiratory illness and that need will become increasingly urgent as the novel H1N1 pandemic influenza virus infects people worldwide.     

Uncertainty and Variability Analysis in Multiplicative Risk Models

Currently, there is a trend away from the use of single (often conservative) estimates of risk to summarize the results of risk analyses in favor of stochastic methods which provide a more complete characterization of risk. The use of such stochastic methods leads to a distribution of possible values of risk, taking into account both uncertainty and variability in all of the factors affecting risk. In this article, we propose a general framework for the analysis of uncertainty and variability for use in the commonly encountered case of multiplicative risk models, in which risk may be expressed as a product of two or more risk factors. Our analytical methods facilitate the evaluation of overall uncertainty and variability in risk assessment, as well as the contributions of individual risk factors to both uncertainty and variability which is cumbersome using Monte Carlo methods. The use of these methods is illustrated in the analysis of potential cancer risks due to the ingestion of radon in drinking water.     

Prioritizing Environmental Health Risks in the UAE

This article presents the results of a comparative environmental risk‐ranking exercise that was conducted in the United Arab Emirates (UAE) to inform a strategic planning process led by the Environment Agency‐Abu Dhabi (EAD). It represents the first national‐level application of a deliberative method for comparative risk ranking first published in this journal. The deliberative method involves a five‐stage process that includes quantitative risk assessment by experts and deliberations by groups of stakeholders. The project reported in this article considered 14 categories of environmental risks to health identified through discussions with EAD staff: ambient and indoor air pollution; drinking water contamination; coastal water pollution; soil and groundwater contamination; contamination of fruits, vegetables, and seafood; ambient noise; stratospheric ozone depletion; electromagnetic fields from power lines; health impacts from climate change; and exposure to hazardous substances in industrial, construction, and agricultural work environments. Results from workshops involving 73 stakeholders who met in five separate groups to rank these risks individually and collaboratively indicated strong consensus that outdoor and indoor air pollution are the highest priorities in the UAE. Each of the five groups rated these as being among the highest risks. All groups rated soil and groundwater contamination as being among the lowest risks. In surveys administered after the ranking exercises, participants indicated that the results of the process represented their concerns and approved of using the ranking results to inform policy decisions. The results ultimately shaped a strategic plan that is now being implemented.