Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost‐benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well‐documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk‐cost‐benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk‐reducing regulations. Low‐regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low‐regret learning strategies using regulation of air pollutants with uncertain health effects as an example.     

Illustration of a Method to Incorporate Preference Uncertainty in Benefit–Cost Analysis

Benefit–cost analysis is widely used to evaluate alternative courses of action that are designed to achieve policy objectives. Although many analyses take uncertainty into account, they typically only consider uncertainty about cost estimates and physical states of the world, whereas uncertainty about individual preferences, thus the benefit of policy intervention, is ignored. Here, we propose a strategy to integrate individual uncertainty about preferences into benefit–cost analysis using societal preference intervals, which are ranges of values over which it is unclear whether society as a whole should accept or reject an option. To illustrate the method, we use preferences for implementing a smart grid technology to sustain critical electricity demand during a 24‐hour regional power blackout on a hot summer weekend. Preferences were elicited from a convenience sample of residents in Allegheny County, Pennsylvania. This illustrative example shows that uncertainty in individual preferences, when aggregated to form societal preference intervals, can substantially change society's decision. We conclude with a discussion of where preference uncertainty comes from, how it might be reduced, and why incorporating unresolved preference uncertainty into benefit–cost analyses can be important.     

Using Probabilistic Terrorism Risk Modeling for Regulatory Benefit-Cost Analysis: Application to the Western Hemisphere Travel Initiative in the Land Environment

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI‐L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk‐reducing effectiveness of WHTI‐L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI‐L, and a range of casualty cost estimates based on the willingness‐to‐pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI‐L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14–26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5–6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit‐cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.     

Are Adult Patients More Tolerant of Treatment Risks Than Parents of Juvenile Patients?

Understanding patient-specific differences in risk tolerance for new treatments that offer improved efficacy can assist in making difficult regulatory and clinical decisions for new treatments that offer both the potential for greater effectiveness in relieving disease symptoms, but also risks of disabling or fatal side effects. The aim of this study is to elicit benefit-risk trade-off preferences for hypothetical treatments with varying efficacy and risk levels using a stated-choice (SC) survey. We derive estimates of "maximum acceptable risk" (MAR) that can help decisionmakers identify welfare-enhancing alternatives. In the case of children, parent caregivers are responsible for treatment decisions and their risk tolerance may be quite different than adult patients' own tolerance for treatment-related risks. We estimated and compared the willingness of Crohn's disease (CD) patients and parents of juvenile CD patients to accept serious adverse event (SAE) risks in exchange for symptom relief. The analyzed data were from 345 patients over the age of 18 and 150 parents of children under the age of 18. The estimation results provide strong evidence that adult patients and parents of juvenile patients are willing to accept tradeoffs between treatment efficacy and risks of SAEs. Parents of juvenile CD patients are about as risk tolerant for their children as adult CD patients are for themselves for improved treatment efficacy. SC surveys provide a systematic method for eliciting preferences for benefit-risk tradeoffs. Understanding patients' own risk perceptions and their willingness to accept risks in return for treatment benefits can help inform risk management decision making.     

Risk‐Based Decision Making for Reoccupation of Contaminated Areas Following a Wide‐Area Anthrax Release

This article presents an analysis of postattack response strategies to mitigate the risks of reoccupying contaminated areas following a release of Bacillus anthracis spores (the bacterium responsible for causing anthrax) in an urban setting. The analysis is based on a hypothetical attack scenario in which individuals are exposed to B. anthracis spores during an initial aerosol release and then placed on prophylactic antibiotics that successfully protect them against the initial aerosol exposure. The risk from reoccupying buildings contaminated with spores due to their reaerosolization and inhalation is then evaluated. The response options considered include: decontamination of the buildings, vaccination of individuals reoccupying the buildings, extended evacuation of individuals from the contaminated buildings, and combinations of these options. The study uses a decision tree to estimate the costs and benefits of alternative response strategies across a range of exposure risks. Results for best estimates of model inputs suggest that the most cost‐effective response for high‐risk scenarios (individual chance of infection exceeding 11%) consists of evacuation and building decontamination. For infection risks between 4% and 11%, the preferred option is to evacuate for a short period, vaccinate, and then reoccupy once the vaccine has taken effect. For risks between 0.003% and 4%, the preferred option is to vaccinate only. For risks below 0.003%, none of the mitigation actions have positive expected monetary benefits. A sensitivity analysis indicates that for high‐infection‐likelihood scenarios, vaccination is recommended in the case where decontamination efficacy is less than 99.99%.     

Should Persons with Diabetes Be Licensed to Drive Trucks?—Risk Management

How should a regulatory agency interpret a risk analysis that concludes there is a small increase in risk? The agency must decide on behalf of society whether the increased risk is large enough to justify banning the risky activity or taking some other step to lessen the risks. In a companion paper (Songer et al.), we conclude that licensing insulin using persons to drive commercial motor vehicles in interstate commerce would result in 42 additional crashes each year. Here we address risk management issues by interpreting the number of additional crashes and the relative risks of the prospective handicapped drivers. Are the number of additional crashes (42) significant? Is the increase in the annual crash risk (from 0.00785 to 0.032 for non-insulin dependent and 0.048 for insulin dependent persons) significant? Are the relative risks significant for all insulin using drivers (4.7)? For drivers with a history of severe hypoglycemic reactions (19.8)? How should society tradeoff risk increases for increases in opportunity for these handicapped persons? We review other social decisions concerning highway safety: Accepting the increasing risks of letting 16 year olds drive, allowing extremely light cars, allowing some unsafe highways, and allowing extremely unsafe driving conditions at some times of day. We conclude that the additional risks from insulin using persons are well within the current accepted range of risks. Currently, 70% of states permit insulin using persons to drive trucks within their state. Nonetheless, the social cost, due to fatalities, injuries, and property damage from allowing a person with a history of severe hypoglycemic reactions to drive is more than $19,700 per year.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

Inventory Planning for a Modular Product Family

This paper is motivated by observing that an increasing number of firms are offering modular products assembled with multiple option choices for the consumer. Starting with the PC offerings by Dell which allowed (and still allows) users to configure their product by choosing among multiple choices for each option, the current market place seems to have evolved to a make‐to‐stock scenario where Apple offers its IPAD series with multiple models each with a unique storage size, color, and wireless chip technology. The focus of our work is on determining the optimal stocking level of modular end‐products. Our analysis is based on a benchmark model with the aim of maximizing expected profit subject to an aggregate fill rate constraint as well as variant‐specific individual fill rates under a make‐to‐stock setting. To further assess the robustness of our finding, we consider the extensions of correlated market preferences over options, price‐dependent demand, and alternative probability distributions for characterizing uncertainty in market preferences or aggregate demand. Finally we also show how to extend the single period model into a multiple‐period setting. Through extensive computational analysis, we find that more precise estimates of market preferences for various modular options constitute extremely valuable information that goes beyond the usefulness of forecasts of aggregate market demand. From a practical perspective, this might be indicative of another classic marketing‐operations trade‐off. Offering more options for consumers would be preferred by marketing managers since this would reach more consumers and hence, enhance product sales. On the other hand, the ability to obtaining greater forecast accuracy would decline when the number of options increase. Hence, from an operational perspective, it would be preferred to limit option choices (so that better forecasts can be obtained) since this would lead to lower stocking costs and hence, higher profits.     

Inventory management and cost-benefit analysis: A case study

This paper treats the problem of comparing various stocking and issuing policies for two radioactive isotopes, I-123 and I-131, which are used in nuclear medicine departments for the diagnosis of thyroid disease. The approach is based on using a combination of a cost-benefit analysis to compare the increased operating costs of stocking I-123 versus the increased disease risk of using I-131, and simulation to recreate the operation of a radio-pharmacy which stocks these drugs. The required inputs to the model include: (1) estimates of the disease risks associated with radiation absorbed doses from I-131; (2) estimates of the probability distributions of the appropriate latent periods; (3) empirical distributions of daily patient demand; (4) census data for estimating longevity and individual expected earnings and (5) cost data the stocking costs of the isotopes. The cost benefit results give issuing rules based on the age and sex of the patient, while the simulation is used to compare the effectiveness of the various ordering policies considered (the choice of a particular ordering policy may influence the risk of disease development).     

Further Notes on Public Response to Uncertainty in Risks and Science

Given the prevalence of uncertainty and variability in estimates of environmental health risks, it is important to know how citizens interpret information representing uncertainty in risk estimates. Ranges of risk estimates from a hypothetical industry source elicited divergent evaluations of risk assessors' honesty and competence among New Jersey residents within one mile of one or more factories. A plurality saw ranges of risk estimates as both honest and competent, but with most judging such ranges as deficient on one or both dimensions. They wanted definitive conclusions about safety, tended to believe the high end of the range was more likely to be an accurate estimate of the risk, and believed that institutions only discuss risks when they are "high." Acknowledgment of scientific, as opposed to self-interested, reasons for uncertainty and disputes among experts was low. Attitude toward local industry seemed associated with, if not a cause of, attitudes about ranges of risk estimates. These reactions by industry neighbors appear to replicate the findings of Johnson and Slovic (1995, 1998), despite the hypothetical producer of risk estimates being industry instead of government. Respondents were older and less educated on average than were the earlier samples, but more diverse. Regression analyses suggested attitude toward industry was a major factor in these reactions, although other explanations (e.g., level of scientific understanding independent of general education) were not tested in this study.     

Cost‐Benefit Analysis for Optimization of Risk Protection Under Budget Constraints

Cost‐benefit analysis (CBA) is commonly applied as a tool for deciding on risk protection. With CBA, one can identify risk mitigation strategies that lead to an optimal tradeoff between the costs of the mitigation measures and the achieved risk reduction. In practical applications of CBA, the strategies are typically evaluated through efficiency indicators such as the benefit‐cost ratio (BCR) and the marginal cost (MC) criterion. In many of these applications, the BCR is not consistently defined, which, as we demonstrate in this article, can lead to the identification of suboptimal solutions. This is of particular relevance when the overall budget for risk reduction measures is limited and an optimal allocation of resources among different subsystems is necessary. We show that this problem can be formulated as a hierarchical decision problem, where the general rules and decisions on the available budget are made at a central level (e.g., central government agency, top management), whereas the decisions on the specific measures are made at the subsystem level (e.g., local communities, company division). It is shown that the MC criterion provides optimal solutions in such hierarchical optimization. Since most practical applications only include a discrete set of possible risk protection measures, the MC criterion is extended to this situation. The findings are illustrated through a hypothetical numerical example. This study was prepared as part of our work on the optimal management of natural hazard risks, but its conclusions also apply to other fields of risk management.     

Risk Comparisons, Conflict, and Risk Acceptability Claims

Despite many claims for and against the use of risk comparisons in risk communication, few empirical studies have explored their effect. Even fewer have examined the public's relative preferences among different kinds of risk comparisons. Two studies, published in this journal in 1990 and 2003, used seven measures of "acceptability" to examine public reaction to 14 examples of risk comparisons, as used by a hypothetical factory manager to explain risks of his ethylene oxide plant. This study examined the effect on preferences of scenarios involving low or high conflict between the factory manager and residents of the hypothetical town (as had the 2003 study), and inclusion of a claim that the comparison demonstrated the risks' acceptability. It also tested the Finucane et al. (2000) affect hypothesis that information emphasizing low risks-as in these risk comparisons-would raise benefits estimates without changing risk estimates. Using similar but revised scenarios, risk comparison examples (10 instead of 14), and evaluation measures, an opportunity sample of 303 New Jersey residents rated the comparisons, and the risks and benefits of the factory. On average, all comparisons received positive ratings on all evaluation measures in all conditions. Direct and indirect measures showed that the conflict manipulation worked; overall, No-Conflict and Conflict scenarios evoked scores that were not significantly different. The attachment to each risk comparison of a risk acceptability claim ("So our factory's risks should be acceptable to you.") did not worsen ratings relative to conditions lacking this claim. Readers who did or did not see this claim were equally likely to infer an attempt to persuade them to accept the risk from the comparison. As in the 2003 article, there was great individual variability in inferred rankings of the risk comparisons. However, exposure to the risk comparisons did not reduce risk estimates significantly (while raising benefit estimates), and Conflict-Claim respondents found the risk of the hypothetical factory less acceptable than No-Conflict respondents. Results suggest that neither risk comparisons nor risk acceptability claims are automatically anathema to audiences, but they may have tiny or unintended effects on audience judgments about risky situations.     

Valuation of childhood risk reduction: the importance of age, risk preferences, and perspective.

This article explores two problems analysts face in determining how to estimate values for children's health and safety risk reductions. The first addresses the question: Do willingness‐to‐pay estimates for health risk changes differ across children and adults and, if so, how? To answer this question, the article first examines the potential effects of age and risk preferences on willingness to pay. A summary of the literature reporting empirical evidence of differences between willingness to pay for adult health and safety risk reductions and willingness to pay for health and safety risk reductions in children is also provided. The second dimension of the problem is a more fundamental issue: Whose perspective is relevant when valuing children's health effects—society's, children's, adults‐as‐children, or parents'? Each perspective is considered, followed ultimately by the conclusion that adopting a parental perspective through an intrahousehold allocation model seems closest to meeting the needs of the estimation problem at hand. A policy example in which the choice of perspective affects the outcome of a regulatory benefit‐cost analysis rounds out the article and emphasizes the importance of perspective.     

Assessing the Benefits and Costs of Homeland Security Research: A Risk-Informed Methodology with Applications for the U.S. Coast Guard

This article describes a methodology for risk-informed benefit–cost analyses of homeland security research products. The methodology is field-tested with 10 research products developed for the U.S. Coast Guard. Risk-informed benefit–cost analysis is a tool for risk management that integrates elements of risk analysis, decision analysis, and benefit–cost analysis. The cost analysis methodology includes a full-cost accounting of research projects, starting with initial fundamental research costs and extending to the costs of implementation of the research products and, where applicable, training, maintenance, and upgrade costs. The benefits analysis methodology is driven by changes in costs and risks leading to five alternative models: cost savings at the same level of security, increased security at the same cost, signal detection improvements, risk reduction by deterrence, and value of information. The U.S. Coast Guard staff selected 10 research projects to test and generalize the methodology. Examples include tools to improve the detection of explosives, reduce the costs of harbor patrols, and provide better predictions of hurricane wind speeds and floods. Benefits models and estimates varied by research project and many input parameters of the benefit estimates were highly uncertain, so risk analysis for sensitivity testing and simulation was important. Aggregating across the 10 research products, we found an overall median net present value of about $385 million, with a range from $54 million (5th percentile) to $877 million (95th percentile). Lessons learned are provided for future applications.     

Collaborate or Compete: Examining Manufacturers' Replacement Strategies for a Substance of Concern

The recent proliferation of media reports on substances of concern has increased consumer fears, sparked scientific debate, and highlighted the need for stronger chemical regulations. When a substance of concern is identified (e.g., bisphenol‐A (BPA) in reusable water bottles), manufacturers face difficult trade‐offs in deciding whether to proactively replace the substance in their products or to defer replacement and wait to see if regulation occurs. In this study, we examine when opportunities exist for manufacturers to avoid competitively replacing (i.e., making their replacement decisions on their own), and instead, collaborate to replace a substance of concern. We model a vertically differentiated market consisting of a high‐end manufacturer and a low‐end manufacturer, both of whom sell a product that contains a substance of concern. Our analysis investigates how market dynamics (competition and consumer preferences) and external factors (replacement costs and regulatory uncertainty) influence manufacturers' collaboration, replacement, and pricing decisions. We find that when the manufacturers do not collaborate, the high‐end manufacturer can use the presence of a substance of concern to dominate the market by capturing more demand and often charging a higher price for his product than the low‐end manufacturer. Collaboration is possible when there is either a shared fixed cost savings for both manufacturers or an opportunity for the low‐end manufacturer to benefit his competitive position by motivating the high‐end manufacturer to collaborate. From a consumer perspective, although collaboration reduces consumer exposure to the substance of concern, it can decrease consumer surplus when the replacement substance is very expensive.     

Defragmenting the Regulatory Process†

The regulatory process is often criticized for being cumbersome and slow, much like a computer whose hard drive is fragmented by files no longer used or useful. Like such a computer, the regulatory process contains many requirements of dubious utility. These include the Paperwork Reduction Act, the Regulatory Flexibility Act, and numerous executive orders. While other parts of the regulatory process such as notice and comment and cost‐benefit analysis have received much more academic attention, these other parts of the process deserve examination as well. This article argues that such an examination will reveal that these statutes and executive orders add little of value to the regulatory process while consuming agency resources. An improved requirement for cost‐benefit analysis with distributional analysis could easily replace virtually all of these requirements and improve regulations while reducing the time needed to promulgate regulations.     

Tax and Education Policy in a Heterogeneous‐Agent Economy: What Levels of Redistribution Maximize Growth and Efficiency?

This paper studies the effects of progressive income taxes and education finance in a dynamic heterogeneous‐agent economy. Such redistributive policies entail distortions to labor supply and savings, but also serve as partial substitutes for missing credit and insurance markets. The resulting tradeoffs for growth and efficiency are explored, both theoretically and quantitatively, in a model that yields complete analytical solutions. Progressive education finance always leads to higher income growth than taxes and transfers, but at the cost of lower insurance. Overall efficiency is assessed using a new measure that properly reflects aggregate resources and idiosyncratic risks but, unlike a standard social welfare function, does not reward equality per se. Simulations using empirical parameter estimates show that the efficiency costs and benefits of redistribution are generally of the same order of magnitude, resulting in plausible values for the optimal rates. Aggregate income and aggregate welfare provide only crude lower and upper bounds around the true efficiency tradeoff.     

Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm‐to‐table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food‐safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.