Risk–Risk Tradeoff Analysis of Nuclear Explosives for Asteroid Deflection

To prevent catastrophic asteroid–Earth collisions, it has been proposed to use nuclear explosives to deflect away earthbound asteroids. However, this policy of nuclear deflection could inadvertently increase the risk of nuclear war and other violent conflict. This article conducts risk–risk tradeoff analysis to assess whether nuclear deflection results in a net increase or decrease in risk. Assuming nonnuclear deflection options are also used, nuclear deflection may only be needed for the largest and most imminent asteroid collisions. These are low‐frequency, high‐severity events. The effect of nuclear deflection on violent conflict risk is more ambiguous due to the complex and dynamic social factors at play. Indeed, it is not clear whether nuclear deflection would cause a net increase or decrease in violent conflict risk. Similarly, this article cannot reach a precise conclusion on the overall risk–risk tradeoff. The value of this article comes less from specific quantitative conclusions and more from providing an analytical framework and a better overall understanding of the policy decision. The article demonstrates the importance of integrated analysis of global risks and the policies to address them, as well as the challenge of quantitative evaluation of complex social processes such as violent conflict.     

Risks of Hazardous Waste Sites versus Asteroid and Comet Impacts: Accounting for the Discrepancies in U.S. Resource Allocation

Approximately $6 billion is spent annually in the United States on the cleanup of sites regulated under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA, or Superfund). The current health risks posed by such sites are thought to be quite small; the expenditures are justified primarily as protecting hypothetical future residents of these sites. Approximately 0.05% of this amount, or $3 million, is spent annually by the U.S. government on the detection of asteroids or comets that could strike the earth. Such damaging impacts do occur from time to time--most recently in 1908 in an unpopulated region of Siberia--but no person is confirmed ever to have died as a result. Anticipated impacts over the course of 1 million years would yield deaths that, when annualized, total approximately 4,000 per year. The risk reduction goal for CERCLA is 15 orders of magnitude greater than that for asteroid/comet detection. A modest increase in resources devoted to asteroid detection would greatly increase the chances of early detection of a threatening object, allowing an effective defense to be attempted. This article argues that the much lower risk-to-resources ratio for CERCLA cleanups than for asteroid and comet detection can be explained by four primary factors: (1) the regard for future generations, since CERCLA benefits mainly the unborn; (2) concrete fears, since toxics are much more feared than asteroids or comets; (3) the source of the threat, since toxic contamination is caused by human beings, unlike impacts from space objects; and (4) the greater political constituencies for hazardous waste cleanup than for space object detection.     

Reducing the Risk of Human Extinction

In this century a number of events could extinguish humanity. The probability of these events may be very low, but the expected value of preventing them could be high, as it represents the value of all future human lives. We review the challenges to studying human extinction risks and, by way of example, estimate the cost effectiveness of preventing extinction-level asteroid impacts.     

Assessment of the Regional Economic Impacts of Catastrophic Events: CGE Analysis of Resource Loss and Behavioral Effects of an RDD Attack Scenario

We investigate the regional economic consequences of a hypothetical catastrophic event—attack via radiological dispersal device (RDD)—centered on the downtown Los Angeles area. We distinguish two routes via which such an event might affect regional economic activity: (i) reduction in effective resource supply (the resource loss effect) and (ii) shifts in the perceptions of economic agents (the behavioral effect). The resource loss effect relates to the physical destructiveness of the event, while the behavioral effect relates to changes in fear and risk perception. Both affect the size of the regional economy. RDD detonation causes little capital damage and few casualties, but generates substantial short‐run resource loss via business interruption. Changes in fear and risk perception increase the supply cost of resources to the affected region, while simultaneously reducing demand for goods produced in the region. We use results from a nationwide survey, tailored to our RDD scenario, to inform our model values for behavioral effects. Survey results, supplemented by findings from previous research on stigmatized asset values, suggest that in the region affected by the RDD, households may require higher wages, investors may require higher returns, and customers may require price discounts. We show that because behavioral effects may have lingering long‐term deleterious impacts on both the supply‐cost of resources to a region and willingness to pay for regional output, they can generate changes in regional gross domestic product (GDP) much greater than those generated by resource loss effects. Implications for policies that have the potential to mitigate these effects are discussed.     

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

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

Affective and Cognitive Factors Influencing Sensitivity to Probabilistic Information

In study 1 different groups of female students were randomly assigned to one of four probabilistic information formats. Five different levels of probability of a genetic disease in an unborn child were presented to participants (within‐subject factor). After the presentation of the probability level, participants were requested to indicate the acceptable level of pain they would tolerate to avoid the disease (in their unborn child), their subjective evaluation of the disease risk, and their subjective evaluation of being worried by this risk. The results of study 1 confirmed the hypothesis that an experience‐based probability format decreases the subjective sense of worry about the disease, thus, presumably, weakening the tendency to overrate the probability of rare events. Study 2 showed that for the emotionally laden stimuli, the experience‐based probability format resulted in higher sensitivity to probability variations than other formats of probabilistic information. These advantages of the experience‐based probability format are interpreted in terms of two systems of information processing: the rational deliberative versus the affective experiential and the principle of stimulus‐response compatibility.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first-order reliability method (FORM)^(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. ⁽⁴⁾ on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. ⁽⁴⁾ The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. ⁴ than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.     

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.     

Probability and Possibility‐Based Representations of Uncertainty in Fault Tree Analysis

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic‐possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility‐probability (probability‐possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis 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.     

Assessing the Exacerbations Risk of Influenza‐Associated Chronic Occupational Asthma

The purpose of this article was to conduct a risk‐based study based on a linkage of experimental human influenza infections and fluctuation analysis of airway function to assess whether influenza viral infection was risk factor for exacerbations of chronic occupational asthma. Here we provided a comprehensive probabilistic analysis aimed at quantifying influenza‐associated exacerbations risk for occupational asthmatics, based on a combination of published distributions of viral shedding and symptoms scores and lung respiratory system properties characterized by long‐range peak expiratory flow (PEF) dynamics. Using a coupled detrended fluctuation analysis‐experimental human influenza approach, we estimated the conditional probability of moderate or severe lung airway obstruction and hence the exacerbations risk of influenza‐associated occupational asthma in individuals. The long‐range correlation exponent (α) was used as a predictor of future exacerbations risk of influenza‐associated asthma. For our illustrative distribution of PEF fluctuations and influenza‐induced asthma exacerbations risk relations, we found that the probability of exacerbations risk can be limited to below 50% by keeping α to below 0.53. This study also found that limiting wheeze scores to 0.56 yields a 75% probability of influenza‐associated asthma exacerbations risk and a limit of 0.34 yields a 50% probability that may give a representative estimate of the distribution of chronic respiratory system properties. This study implicates that influenza viral infection is an important risk factor for exacerbations of chronic occupational asthma.     

Representation,Propagation, and Decision Issues in Risk Analysis Under Incomplete Probabilistic Information

This article tries to clarify the potential role to be played by uncertainty theories such as imprecise probabilities, random sets, and possibility theory in the risk analysis process. Instead of opposing an objective bounding analysis, where only statistically founded probability distributions are taken into account, to the full‐fledged probabilistic approach, exploiting expert subjective judgment, we advocate the idea that both analyses are useful and should be articulated with one another. Moreover, the idea that risk analysis under incomplete information is purely objective is misconceived. The use of uncertainty theories cannot be reduced to a choice between probability distributions and intervals. Indeed, they offer representation tools that are more expressive than each of the latter approaches and can capture expert judgments while being faithful to their limited precision. Consequences of this thesis are examined for uncertainty elicitation, propagation, and at the decision‐making step.     

How Probabilistic Risk Assessment Can Mislead Terrorism Risk Analysts

Traditional probabilistic risk assessment (PRA), of the type originally developed for engineered systems, is still proposed for terrorism risk analysis. We show that such PRA applications are unjustified in general. The capacity of terrorists to seek and use information and to actively research different attack options before deciding what to do raises unique features of terrorism risk assessment that are not adequately addressed by conventional PRA for natural and engineered systems—in part because decisions based on such PRA estimates do not adequately hedge against the different probabilities that attackers may eventually act upon. These probabilities may differ from the defender's (even if the defender's experts are thoroughly trained, well calibrated, unbiased probability assessors) because they may be conditioned on different information. We illustrate the fundamental differences between PRA and terrorism risk analysis, and suggest use of robust decision analysis for risk management when attackers may know more about some attack options than we do.     

A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis

In risk analysis, the treatment of the epistemic uncertainty associated to the probability of occurrence of an event is fundamental. Traditionally, probabilistic distributions have been used to characterize the epistemic uncertainty due to imprecise knowledge of the parameters in risk models. On the other hand, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for parameters for which the information available is scarce and of qualitative nature. In practice, it is to be expected that a risk model contains some parameters affected by uncertainties that may be best represented by probability distributions and some other parameters that may be more properly described in terms of fuzzy or possibilistic distributions. In this article, a hybrid method that jointly propagates probabilistic and possibilistic uncertainties is considered and compared with pure probabilistic and pure fuzzy methods for uncertainty propagation. The analyses are carried out on a case study concerning the uncertainties in the probabilities of occurrence of accident sequences in an event tree analysis of a nuclear power plant.     

Integration of Probabilistic Exposure Assessment and Probabilistic Hazard Characterization

A method is proposed for integrated probabilistic risk assessment where exposure assessment and hazard characterization are both included in a probabilistic way. The aim is to specify the probability that a random individual from a defined (sub)population will have an exposure high enough to cause a particular health effect of a predefined magnitude, the critical effect size ( CES ). The exposure level that results in exactly that CES in a particular person is that person's individual critical effect dose ( ICED ). Individuals in a population typically show variation, both in their individual exposure ( IEXP ) and in their ICED . Both the variation in IEXP and the variation in ICED are quantified in the form of probability distributions. Assuming independence between both distributions, they are combined (by Monte Carlo) into a distribution of the individual margin of exposure ( IMoE ). The proportion of the IMoE distribution below unity is the probability of critical exposure ( PoCE ) in the particular (sub)population. Uncertainties involved in the overall risk assessment (i.e., both regarding exposure and effect assessment) are quantified using Monte Carlo and bootstrap methods. This results in an uncertainty distribution for any statistic of interest, such as the probability of critical exposure ( PoCE ). The method is illustrated based on data for the case of dietary exposure to the organophosphate acephate. We present plots that concisely summarize the probabilistic results, retaining the distinction between variability and uncertainty. We show how the relative contributions from the various sources of uncertainty involved may be quantified.     

Direct Estimation of the Cost Effectiveness of Tornado Shelters

This article estimates the cost effectiveness of tornado shelters using the annual probability of a tornado and new data on fatalities per building struck by a tornado. This approach differs from recent estimates of the cost effectiveness of tornado shelters in Reference 1 that use historical casualties. Historical casualties combine both tornado risk and resident action. If residents of tornado-prone states take greater precautions, observed fatalities might not be much higher than in states with lower risk. Estimation using the tornado probability avoids this potential bias. Despite the very different method used, the estimates are 68 million US dollars in permanent homes and 6.0 million US dollars in mobile homes in Oklahoma using a 3% real discount rate, within about 10% of estimates based on historical fatalities. The findings suggest that shelters provide cost-effective protection for mobile homes in the most tornado-prone states but not for permanent homes.     

Probabilistic Reliability Modeling for Oil Exploration & Production (E&P) Facilities in the Tallgrass Prairie Preserve

The aging domestic oil production infrastructure represents a high risk to the environment because of the type of fluids being handled (oil and brine) and the potential for accidental release of these fluids into sensitive ecosystems. Currently, there is not a quantitative risk model directly applicable to onshore oil exploration and production (E&P) facilities. We report on a probabilistic reliability model created for onshore exploration and production (E&P) facilities. Reliability theory, failure modes and effects analysis (FMEA), and event trees were used to develop the model estimates of the failure probability of typical oil production equipment. Monte Carlo simulation was used to translate uncertainty in input parameter values to uncertainty in the model output. The predicted failure rates were calibrated to available failure rate information by adjusting probability density function parameters used as random variates in the Monte Carlo simulations. The mean and standard deviation of normal variate distributions from which the Weibull distribution characteristic life was chosen were used as adjustable parameters in the model calibration. The model was applied to oil production leases in the Tallgrass Prairie Preserve, Oklahoma. We present the estimated failure probability due to the combination of the most significant failure modes associated with each type of equipment (pumps, tanks, and pipes). The results show that the estimated probability of failure for tanks is about the same as that for pipes, but that pumps have much lower failure probability. The model can provide necessary equipment reliability information for proactive risk management at the lease level by providing quantitative information to base allocation of maintenance resources to high-risk equipment that will minimize both lost production and ecosystem damage.     

A Solar-Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections

We present a solar-centric approach to estimating the probability of extreme coronal mass ejections (CME) using the Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph Experiment (LASCO) CME Catalog observations updated through May 2018 and an updated list of near-Earth interplanetary coronal mass ejections (ICME). We examine robust statistical approaches to the estimation of extreme events. We then assume a variety of time-independent distributions fitting, and then comparing, the different probability distributions to the relevant regions of the cumulative distributions of the observed CME speeds. Using these results, we then obtain the probability that the velocity of a CME exceeds a particular threshold by extrapolation. We conclude that about 1.72% of the CMEs recorded with SOHO LASCO arrive at the Earth over the time both data sets overlap (November 1996 to September 2017). Then, assuming that 1.72% of all CMEs pass the Earth, we can obtain a first-order estimate of the probability of an extreme space weather event on Earth. To estimate the probability over the next decade of a CME, we fit a Poisson distribution to the complementary cumulative distribution function. We inferred a decadal probability of between 0.01 and 0.09 for an event of at least the size of the large 2012 event, and a probability between 0.0002 and 0.016 for the size of the 1859 Carrington event.