Risk Analysis, Vol. 19, No. 4, 1999

Editorial on the Nugget Papers

Invited papers called Nugget papers were pre-sented to a large audience during extended specialsessions at the 1997 Annual Meeting of the Societyfor Risk Analysis. This special issue of our flagshipjournal includes most of these Nugget papers. Theyrepresent the state of the art of the various branchesof risk analysis and constitute a compendium ofknowledge, experience, and wisdom that can serveall who are engaged in the art and science of riskassessment, communication, and management. Intheir totality, these papers remind us that risk analysishas emerged as an effective and comprehensive pro-cess that supplements and complements the overallmanagement of almost all aspects of our lives. Man-agers of health care, the environment, and physicalinfrastructure systems (e.g., water resources, trans-portation, and electric power, to cite a few) all incor-porate risk analysis in their decision-making pro-cesses. The omnipresent adaptation of risk analysisby many disciplines and its employment in decision-making by industry and government agencies haveled to an unprecedented development of theory,methodology, and practical tools, samples of whichare presented in these Nugget papers.

The challenge that society faces today is that allthis knowledge has not been fully duplicated, shared,and transferred from one field of endeavor to an-other. This calls for a concerted effort to improve ourunderstanding of the commonalities and differencesamong fields for the mutual benefit of society as awhole. Such a transfer of knowledge, and the Societyfor Risk Analysis is proud to share in it, has alwaysbeen the key to the advancement of the natural, so-cial, and behavioral sciences and of engineering. Wecan start meeting this challenge through our collegeand university classrooms and through continuing ed-ucation programs in industry and government. It isessential to build bridges among the disciplines andto facilitate the process of learning from each other.

For risk assessment, communication, and man-agement to be meaningful, they must be an integralpart of an overall holistic consideration of the entireproblem or (system) being analyzed. This holisticapproach stems from the realization that each ofthese processes is a blend of art and science; althoughmathematical formulation and modeling of a prob-lem are important for sound decision-making, they

6850272-4332/99/0800-0685$16.00/1 1999 Society for Risk Analysis

are not by themselves sufficient for that purpose.These papers clearly document that institutional, or-ganizational, managerial, political, and cultural con-siderations, among others can be as dominant as sci-entific, technological, economic, or financial aspects,and must be accounted for in the decision-makingprocess. In this context, however, it is important tokeep in mind Einstein’s statement: ‘‘So far as thetheorems of mathematics are about reality, they arenot certain; so far as they are certain, they are notabout reality.’’ By projecting Einstein’s statement tothe field of risk assessment and management, and tokeep our expectations in check, we assert that:

To the extent that risk assessment is precise, it isnot real.To the extent that risk assessment is real, it is notprecise.

Six Nugget papers are presented here. In hischallenging and visionary paper: ‘‘Trust, Emotion,Sex, Politics, and Science: Surveying the Risk Assess-ment Battlefield,’’ Paul Slovic argues that there is aneed for a new approach to risk analysis that wouldbe responsive to the limitations of risk science. Sucha new approach would focuses upon ‘‘introducingmore public participation into both risk assessmentand risk decision-making in order to make the deci-sion process more democratic, improve the relevanceand quality of technical analysis, and increase thelegitimacy and public acceptance of the resulting de-cisions.’’ He also discusses the importance and diffi-culty of maintaining trust and the subjective andcontextual natures of the risk game. On thequantification of risk, Slovic reminds us with amplereferences that in general social science analysis re-jects the notion that the probabilities and conse-quences of adverse events can be ‘‘assumed to beproduced by physical and natural processes in waysthat can be objectively quantified by risk assess-ment.’’ In this view, Slovic argues ‘‘risk does not exist‘out there,’ independent of our minds and cultures,waiting to be measured.’’

Vicki Bier addresses the challenges facing ana-lysts in the quantification of risk, especially the exten-sive reliance on subjective judgment and aspects ofhuman performance, including ‘‘not only human er-ror per se but also management and organizational

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factors more broadly.’’ To further advance the stateof knowledge and practice of PRA, in her paper titled‘‘Challenges to the Acceptance of Probabilistic RiskAnalysis (PRA),’’ Bier calls for a closer and thusmore ‘‘fruitful collaboration with researchers fromother disciplines, such as Bayesian statistics, indus-trial psychology, or organizational behavior and de-sign.’’ Although Bier argues that PRA has advancedto the point where it is being productively applied ina variety of fields, a baffling range of questions re-mains in the area of organizational behavior. Shewrites: ‘‘For example, we do not yet fully understandthe implications of voluntary vs. mandatory incident-reporting systems in practice, yet important risk-man-agement decisions in a variety of fields are madebased on data collected from such systems.’’

In another seminal paper titled ‘‘Human Interin-dividual Variability in Parameters Related to HealthRisks,’’ Dale Hattis, Prerna Banati, Rob Goble, andDavid Burmaster address the modeling aspects ofhealth-risk analysis, focusing on the variability of sys-tem and model parameters. They remind us of thedistinction between the variability and uncertaintyconcepts: ‘‘Variability means the distribution of somereal quantity among things or people even after theapplication of perfect measurement techniques,whereas uncertainty is a description of the imperfec-tion of our information about a parameter (includinga parameter describing real variability).’’ For the con-duct of health-risk analyses, the authors present, withample illustrations, discrete steps in what they terma ‘‘pathway’’ leading from external exposure to theproduction of biological responses. They predict thatin the future ‘‘a variety of distributional analyses arelikely to expand further into areas that have beendominated by qualitative approaches and heuristi-cally-determined point-estimate procedures.’’ Theyfurther argue that this transition ‘‘is likely, in the longrun, to make the results of analyses more useful todecision-makers and the public, but will, of course,require learning and interchange on the part of boththe analysts and their audiences.’’

Paul Kleindorfer and Howard Kunreuther de-scribe the intimate interface between risk analysis andpractice, where the distinctive borders between thequantitative and qualitative aspects of public policyissues are often indistinguishable, if not actually disap-pearing. In their paper titled ‘‘The ComplementaryRoles of Mitigation and Insurance in Managing Cata-strophicRisks,’’KleindorferandKunreutherexamine‘‘the impact that insurance coupled with specific risk-mitigation measures (RMMs) could have on reducing

losses from hurricanes and earthquakes as well as im-proving the solvency position of insurers who providecoverage against these hazards.’’ They maintain thatfor RMM to be effective, the property owner shouldview the cost as an attractive investment, the insurershould expect that the claim payments will be reducedshould a disaster strike the area, and the contractorand developer should find it easier to sell more well-designed property even if it costs more.’’ Convincedof the efficacy of the complementary roles of mitiga-tion and insurance in managing extreme and cata-strophic risks from major natural disasters, such asearthquakes and hurricanes, Kleindorfer and Kun-reuther offer a list of future research directions in thisarea. They encourage the pursuit of studies on regula-tory and uncertainty issues, on the tradeoffs betweenreinsurance and mitigation, and on the impact of miti-gation on capital market instruments.

The safe disposal of nuclear waste has been onthe national, and indeed, the international agendafor several decades now. In a succinct paper titled‘‘A Perspective on Nuclear Waste,’’ Warner Northoffers a valuable insight into this thorny issue froma risk analysis perspective. He argues that the ‘‘man-agement of spent fuel and high-level nuclear wastehas the deserved reputation as one of the most intrac-table policy issues facing the United States and othernations using nuclear reactors for electric power gen-eration.’’ Calling on the Society for Risk Analysis todevelop a better understanding of how to assess andmanage risks from nuclear waste, he maintains thatthe problem is more of perception than of the needfor a ‘‘technical fix.’’ According to North this differ-ence in perception among the parties responsible forthe disposal of nuclear waste has led, ‘‘to an erosionof trust in scientists and in the institutions that havebeen given responsibility for dealing with nuclearwaste.’’ North compares the use of performance as-sessment of nuclear waste disposal with fate andtransport modeling of air quality and groundwatercontamination. He argues that while ‘‘the calcula-tions are conceptually similar, the time scales arequite different: Air quality models are run for a pe-riod of hours or a few days . . . Groundwater modelsaddress time periods of days to several hundredyears . . . Nuclear waste performance assessmentmodels usually address time periods of thousands tomillions of years.’’ These differences in assessmentshave led the National Research Council to cautionthat risks at the Yucca Mountain site from futurehuman intrusion are not susceptible to analysis in thesame manner as risks from geological processes.

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The last, but certainly not least, in this set ofNugget papers is a tutorial article by Roman Krzysz-tofowicz, titled ‘‘Bayesian Forecasting via Determin-istic Model.’’ Krzysztofowicz emphasizes the limita-tions of deterministic models of complex, nonlinear,and dynamic systems for decision-making purposes.To overcome this deficiency, he maintains that the‘‘Bayesian forecasting system offers a theoreticalfoundation and an operational framework for proba-bilistic forecasting via deterministic model of an arbi-trary complexity.’’ Krzysztofowicz substantiates thisassertion in a cogent and pedagogically-informativediscussion and by presenting two approaches to prob-abilistic forecasting via deterministic models. Thefirst approach couples a deterministic model with apost-processor. The second approach formulates aprobabilistic forecasting system, which includes an

input forecaster, a deterministic model, a co-pro-cessor, a post-processor, and an integrator. Amongthe many interesting findings is that the Bayesianforecasting system presented in this paper ‘‘decom-poses the total uncertainty about the predictant intoinput uncertainty and model uncertainty. Each uncer-tainty is quantified independently of the other andthen both are integrated into a probabilisticforecast.’’

In closing, I want to thank the authors of thesepapers and all the presenters at the Nugget sessionsthat were held during the 1997 SRA Annual Meetingfor their valuable contributions to the advancementof our knowledge in risk analysis.

Yacov Y. HaimesCharlottesville, Virginia