Ten Most Important Accomplishments in Risk Analysis, 1980–2010

As part of the celebration of the 30th anniversary of the Society for Risk Analysis and Risk Analysis, An International Journal, a group of your editors engaged in a process to select the 10 most important accomplishments in risk analysis. The article that follows is the product of this process. Some preliminary decisions were that we would reach out to the full membership for nominations, focus on the period 1980 to 2010, and accept nominations for contributions to theory, methods, and applications. Also, we focused on accomplishments that address health, safety, and the environment, which has been our tradition.(1) All the accomplishments have contributed to answering at least one of the six following risk analysis questions:(2-5) What can go wrong? What are the chances that something with serious consequences will go wrong? What are the consequences if something does go wrong? How can consequences be prevented or reduced? How can recovery be enhanced, if the scenario occurs? How can key local officials, expert staff, and the public be informed to reduce concern and increase trust and confidence? Four caveats are in order. First, the list does not imply a ranking of importance. Second, we acknowledge that there were many other meritorious contributions. Our list of 10 is based on what we received from the SRA membership, with some revision in the descriptions of the contributions, based on the judgment of the editors. After listing the 10, we present a synopsis of each, state its contribution and its significance, and note some of the key studies. Third, we acknowledge that we have deliberately and disproportionately cited articles from Risk Analysis to illustrate the contributions. Fourth, these short presentations are not full recitations of the idea, nor do we pretend to provide all the key citations. Indeed, we expect to publish perspectives from SRA members about these and other important contributions during the past 30 years and prospects for the next decade. Understanding how affect and trust influence risk perception and behavior Recognizing that personal decisions reflect different processes for valuing and combining anticipated and actual losses, gains, delays, and surprises. Developing an environmental justice ethic and frameworks Using formal uncertainty analysis in risk assessment Building the capacity and frameworks to apply multiobjective decision making to complex decisions that trade off risk and other criteria Modifying existing regional economic impact assessment tools Estimating likelihoods of events across a broad spectrum of hazard events Applying intelligent agent models to terrorism Building an applied field of risk communications Making risk-informed legal and regulatory decisions Psychologists have contributed to our understanding of risk perceptions and beliefs. The “affect” heuristic—whereby a fast, intuitive, and emotional response anchors initial evaluations of risky prospects, which slower cognitive pathways may then adjust—and trust are at the heart of their efforts. About half of the 20 most cited articles in the journal are the product of these endeavors. Affect is negative or positive feelings and emotions about a hazard, or, more generally, in response to any activity, object, or stimulus. Some assert that we should base our risk perceptions on rational deliberation. But research shows that our perceptions are heavily influenced by what we feel about a hazard and the people who manage it.(6) The literature on applications of the affect heuristic now includes studies of smoking, gambling, investing, drug use, and many other activities. It shows that affect often leads people to hard-to-shake perceptions that do not follow from rational deliberation, and indeed are sometimes contrary to personal self-interest. In other words, there are two types of thinking, one “analytical” that is grounded in deliberative analysis of risk and a second that is “experiential” based on intuitive, fast, image-based affective reaction to danger.(7-10) Mixing affect with analytical deliberations has had a profound impact on our ability to understand personal risk analyses, choices, and behaviors. No less influential has been the probing of the relationship between trust and perception. Many people lack information and do not connect emotionally to an issue, yet they may connect through what they believe about authorities who manage the risk and issue warnings. If risk managers are perceived to be competent at assessing and managing risk and to be honest, caring, and accessible to the public, then less concern is felt and more benefits are perceived, leading to greater acceptance of a hazard.(11-16) How heavily individuals weight the messages and management of authorities in reducing their concerns about hazards varies predictably with political ideology.(17) Trust can be built and it can be lost.(14) From food-related risks to nuclear waste management, assessing trust in specific decisionmakers and purveyors of information has been shown to be important.(16) An ongoing important endeavor is to understand the relative and combined contributions of affect and trust. Utility theory assumes that people seek personally optimal solutions, are self-interested, forward looking, and rely on consistent and rational decision-making processes. If that were the case, and if adequate information were available at no or little cost, then more than 46% of the U.S. adult population would have a last will and testament, including some people with considerable resources who do not;(18) every driver would use seatbelts; no one would smoke (unless they had strong biochemical and genetic information that they were smoke-tolerant), people living in areas that are prone to natural hazard events would have insurance, and so on. Amos Tversky and Daniel Kahneman's prospect theory posits that, in the face of uncertain risks, people group and order options and then create individual value functions for each option.(19,20) These functions are nonlinear (and steeper for losses than for gains) because we mind losing what we already have more than we mind not winning what we do not yet have. Instead of focusing on final outcomes such as accumulated wealth, people focus on changes around a reference point—gains and losses from their personal status quo point or other aspiration level. Furthermore, low probability outcomes are overweighted and moderate to high ones are underweighted, although certainty is overweighted compared to near-certainty. Prospect theory has been featured primarily in the economics literature, and indeed Kahneman and Tversky's 1979 paper is the most cited paper in Econometrica.(19) During the last 30 years, almost 200 articles in Risk Analysis have used prospect theory to better understand environmental and public health risks. The first major article about prospect theory in the journal in 1983 derived hypothetical weights and functions to predict reactions to rare events with a focus on possible problems at nuclear energy facilities.(21) A 2011 publication in Risk Analysis reports that employees of the USDA Forest Service who have the authority to choose how to manage wildfire events chose options that avoid loss, selecting the safe option more often when the consequences of the choice were framed as potential gains.(22) The subjects also exhibited discounting, choosing to minimize short-term over long-term risk due to a belief that future risk could be controlled. There is a massive and growing literature on applications of prospect theory because it resonates so well with behavior.(23-28) Prospect theory, however, should not be regarded as a guide for normative decision making, as noted in Kahneman's 2011 book.(29) Rather, decisionmakers should reflect carefully on the extent that the zero point and regret should enter the evaluation of gains and losses. While prospect theory may be better than traditional (e.g., Bernoulli's) utility theory in describing how people actually make choices, individuals and organizations should be urged to evaluate outcomes carefully in terms of long-term rather than short-term goals. Normative valuation across a multiplicity of criteria remains a challenge, especially in a social or group decision context. As Kenneth Arrow showed with his Impossibility Theorem,(30) a group preference function reflecting individual preferences may not exist. In 1994, U.S. President Clinton issued Executive Order 12898 requiring federal agencies and departments to develop environmental justice (EJ) strategies for administration of environmental rules and guidelines, including hiring. Every environmental impact statement considers EJ, and prudent private and public managers routinely consider EJ before they pick a location for a new facility or make major operational changes at existing sites. Executive Order 12898 had its roots in the Civil Rights Act of 1964. Similar efforts occurred across the globe from the 1950s through the 1980s. In the United States, the strongest arguments for EJ were made by the United Church of Christ (UCC), the only religious organization that still had a civil rights thrust in the late 1980s. Benjamin Chavis, Jr. coined the expression “environmental racism” and Charles Lee of the UCC built the arguments and a database to support the theory. The report Toxic Waste and Race(31) showed that the concentration of hazardous waste sites was strongly associated with ethnicity/race and socioeconomic status, and was the basis upon which to assert inequity in the distribution of environmental risks and benefits. The EJ literature is massive, including nearly 200 papers in Risk Analysis that directly focus on EJ or invoke the theory to make an argument. Notably, the literature is equivocal about the empirical evidence.(32-37) For example, Zimmerman(32) studied Superfund (NPL) sites and observed that these disproportionately were located in minority areas, but that this same finding did not apply for socioeconomic status. Cutter et al.(37) used the Toxic Release Inventory and other data to examine the distribution of polluting industrial facilities in South Carolina, and did not observe a disproportionate burden on racial minorities and the poor. Burger et al.(38) argue that all populations should have access to the decision-making process and provide suggestions for ecological information. Greenberg(39) argued and demonstrated that markedly different results could be produced by manipulating the geographical scale of the data, time period, definitions of populations, and statistics used to measure inequity. Nevertheless, the explication of the theory in the context of civil rights has been a powerful device to challenge policies about transportation, energy, and cleanup priorities after environmental disasters (e.g., Katrina). EJ theory may be the most important theoretical addition to environmental policy over the last two decades because it forces decisionmakers to consider distributive impacts and nonquantifiable costs and benefits.(40-42) Rules, regulations and guidelines about human and ecological risks historically have been based on deterministic analyses and conservative safety factors. However, decisionmakers might make different decisions if informed about uncertainty in the underlying data. Formal Bayesian methods allow users to incorporate both prior knowledge and experimental data into risk assessments.(43-45) Uncertainty in multiple inputs to a deterministic analysis can be described by a set of probability distributions on these inputs, which may need a conditional structure because some inputs depend upon others. Monte Carlo simulation has become a widely used practice for calculating a probability distribution on model output from the set of probability distributions on model inputs. Decision trees and influence diagrams (or Bayesian networks(46)) are also used for such calculations, and have the advantage that the conditional structure is made explicit. These types of formal uncertainty analyses have become widely used for understanding plausible risks and identifying critical uncertainties associated with them for U.S. federal agencies, and in the European Union and Japan, among other nations.(46-51) Some subnational government agencies now mandate the use of probabilistic risk assessment (PRA) and provide guidance on its use. Thousands of government documents and journal articles, including over 500 articles in Risk Analysis, have documented the evolution of PRA and excellent summary papers have been written.(52-54) Guidance and practice suggest that this method is used after simpler approaches indicate there is a need to be concerned. Important refinements have been suggested, such as separating analyses into components that the decisionmaker can control and those that it cannot. Examples abound, including assessments of contaminated sediments in water bodies, cancer risk assessment, and many others.(55,56) In Risk Analysis, beginning with the very first article in which Kaplan and Garrick(2,57) defined the risk assessment trilogy, the major focus was nuclear energy and waste management. Individual articles have focused on reactor safety, loss of off-site power, the modeling of a variety of initiating event issues associated with radioactive waste repositories, fires, precursor events, and health effects from Chernobyl.(58-67) All U.S. nuclear power plants have been required to prepare risk assessments that rely on probabilistic simulations and this practice has spread. Probabilistic risk analysis has limitations. Data requirements are substantial. Decisionmakers and/or their staff need some background in these methods in order to understand that options are clarified and not obfuscated, and some distrust the method because they assume that values are buried in the numbers and that these drive the decisions. Nevertheless, many government bodies and private organizations have become more committed to the method and have issued guidelines that eliminate some of the concerns of the early years. It appears to us that with the emergence of guidance and off-the-shelf software packages, the use of the method will continue to diffuse. The ability to assess the influence of multiple criteria as part of an analytical decision-making process has appealed to analysts and some decisionmakers for decades.(68) Globalization, population growth, and increasing pressure on resources and the environment have increased the potential value of multiattribute decision support tools and models. Analysts have responded with multiple methods, such as the analytic hierarchy process, data envelopment, goal programming, multiple-attribute utility theory, value engineering, and a host of others.(69,70) Yet there has been skepticism about this class of models, some asserting that they are too expensive to build, use, and maintain, inflexible in the face of multiple agency needs, and difficult to understand. For example, a widely cited paper in urban planning characterized these models as dinosaurs that collapsed rather than evolved.(71) One reason for this label was the models’ inability to quickly solve the problems. This remains an issue but considerable progress has been made. The key challenge has been overcoming objections that the models try to be too comprehensive and thereby become incomprehensible. We have clear evidence that this issue can be addressed. Risk Analysis has published about 100 articles that apply one or more multiobjective decision-making tools to topics such as facility siting, waste management, and homeland security.(72-80) For example, Linkov et al. used multicriteria decision analysis (MCDA) to examine alternative ways of managing contaminated sediments.(72) The authors built an MCDA approach that explicitly wove together expert judgment and stakeholder views. A goal for developers of homeland security applications has been to make them both comprehensive and comprehensible. For example, Leung et al.(77) developed a two-stage approach aimed at determining how to safeguard bridges against terrorist attacks. Building on the hierarchical holographic modeling of Haimes, they examined and prioritized risks and then considered risk management responses. Li et al.(80) built a simulation model of the MIT campus using value trees based on multiattribute utility theory. In both of these and other cases, the models provide entry for decisionmakers’ preferences about the value and potential impacts of options. Far from the black box models that repulsed potential users, these user-friendly efforts do not require advanced degrees in mathematics to follow and apply. They signal the possibility of many more directly useful public policy applications. Multipliers, econometric, and input models have been used to estimate impacts of new steel mills, airports, road projects, and other large projects on regional economic product, jobs, taxes, and population migration. These tools depend on historical relationships embedded in government-collected economic data. However, in the case of hazard events, the inability to take into account precipitous loss of economic capacity can to economic and assessments of the benefits of to reduce models and models are two simulation approaches that address these limitations. models focus on the of most directly by the and of these are and the impacts through the for example, if a a major the impacts would be on the and that upon that all of the that from the or their through the would be might their jobs, leading to an After the if there are no more the and a new models assume a economic and use simulation models to estimate of and for an If the an economic such as a terrorist on a or a the water the model to with the most to make the The models can explicitly incorporate in the which is a major For example, built a model to the impact of an on the water and in on the of the The model the and of the water as well as water use These the negative regional economic and models are for the economic consequences of hazard events, yet both have requiring use of about among others. 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In we that risk communications the to and the public, for example, public to be forward 30 years to a large of theory and empirical evidence that that risk is and useful information to all including and managers who too often that they already they need to that the of personal feelings of and other is critical and that risk need to their messages to address and risk information is not only but can be leading to public perceptions and behaviors. 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The need for risk-informed decisions is across the globe and an ongoing challenge for every of the risk analysis and this article has been both an and The were in choosing among so many important contributions and then each in about We acknowledge that may have alternative nominations and about those we We to us to the note that we have in each of the 10 areas to perspectives to in future issues on new in these We members who topics to and we note that the do not an of the Society of Risk