Skip to main content Accessibility help
Hostname: page-component-59f8fd8595-p59nl Total loading time: 0 Render date: 2023-03-22T01:19:29.368Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

6 - Uncertain Risk: The Role and Limits of Quantitative Assessment

Published online by Cambridge University Press:  05 June 2012

Alison C. Cullen
The Daniel J. Evans School of Public Affairs, University of Washington, Seattle, WA
Mitchell J. Small
Departments of Civil Engineering & Environmental Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA
Timothy McDaniels
University of British Columbia, Vancouver
Mitchell Small
Carnegie Mellon University, Pennsylvania
Get access



The assessment and management of uncertain risks is an essential component of science, engineering, medicine, business, law, and other modern disciplines. I. Good argues that risk science based on the evaluation of probabilistic events has been useful in saving and improving lives throughout history, “since the assessment of uncertainty incorporates the idea of learning from experience which most creatures do” (Good, 1959). Learning from experience is only part of the challenge, since many decisions involve new or evolving risk scenarios in which humans have little or no experience. Risk management capabilities must include the ability to respond to risks that are known and well understood, and the ability to detect and anticipate those that are newly emerging (e.g., U.S. EPA SAB, 1995; NRC, 1999; U.S. EPA ORD, 2000). Uncertainty analysis provides a framework for characterizing the position of a problem along this continuum and the implications for appropriate response and management.

Uncertainty about risk arises as a result of a fundamental lack of understanding about how the world works, randomness in the outcome of natural and human processes, and an inability to measure, characterize, or even at times define key quantities of interest that affect, or are affected by, risk. Risks can vary across time, space, and from individual to individual in a population. Characterization of these variations is an essential part of a risk analysis; however, the basic knowledge or data to fully and effectively characterize risk, and its variability, are often lacking.

Risk Analysis and Society
An Interdisciplinary Characterization of the Field
, pp. 163 - 212
Publisher: Cambridge University Press
Print publication year: 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


Adlassnig, K.-P. 1986. Fuzzy set theory in medical diagnosis. IEEE Transactions on Systems, Man and Cybernetics, SMC-16: 260–5CrossRefGoogle Scholar
Andrews, R. 1997. Risk-based decision making. In Environmental Policy in the 1990s, N. Vig and M. Kraft, eds., Congressional Quarterly, 208–230, Washington, DC
Apostolakis, G. E. and Pickett, S. E.. 1998. Deliberation: Integrating analytical results into environmental decision involving multiple stakeholders. Risk Analysis, 18 (5): 621–47CrossRefGoogle Scholar
Ashby, D., and Hutton, J. L. 1996. Bayesian epidemiology. In D. K. Berry, ed., Bayesian Biostatistics, pp. 109–38. Marcel Dekker, New York
Bankes, S. C. 1993. Exploratory modeling for policy analysis. Operations Research, 41: 435–49CrossRefGoogle Scholar
Bankes, S. C. 1994. Computational experiments and exploratory modeling. Chance, 7 (1): 50–1, 57Google Scholar
Bedford, T., and R. Cooke. 2001. Probabilistic Risk Analysis: Foundations and Methods, Cambridge University Press, Cambridge
Berry, D. A. and Stangl, D. K., eds. 1996. Bayesian Biostatistics. Marcel Dekker, New York
Bier, V. M., Haimes, Y. Y., Lambert, J. H., Ferson, S., and Small, M. J. 2003. Quantifying risk of extreme or rare events: Lessons from a selection of approaches. This volume.
Bier, V. M., Haimes, Y. Y., Lambert, J. H., Matalas, N. C., and Zimmerman, R. 1999. A survey of approaches for assessing and managing the risk of extremes. Risk Analysis, 19 (1): 83–94CrossRefGoogle Scholar
Bogen, K. T. 1990. Uncertainty in Environmental Health Risk Assessment, Garland Publishing, New York
Bogen, K. T. 1995. Methods to approximate joint uncertainty and variability in risk. Risk Analysis, 15 (3): 411–19CrossRefGoogle Scholar
Bostrom, A., Fischhoff, B., and Morgan, M. G. 1992. Characterizing mental models of hazardous processes: A methodology with an application to radon. J. Social Issues, 48 (4): 85–100CrossRefGoogle Scholar
Boudet, C., Zmirou, D., Laffond, M., Balducci, F., and Benoit-Guyod, J-L. 1999. Health risk assessment of a modern municipal waste incinerator. Risk Analysis, 19: 1215–22CrossRefGoogle ScholarPubMed
Brasquet, C., Bourges, B., and Cloirec, P. 1999. Quantitative structure-property relationship (QSPR) for the adsorption of organic compounds onto activated carbon cloth: Comparison between multiple linear regression and neural network. Environmental Science and Technology, 33 (23): 4226–31CrossRefGoogle Scholar
Brattin, W. J., Barry, T. M., and Chiu, N. 1996. Monte Carlo modeling with uncertain probability density functions. Human and Ecological Risk Assessment, 2 (4): 820–40CrossRefGoogle Scholar
Brieman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. 1984. Classification and regression trees. Wadsworth&Brooks/Cole Advanced Books & Software, Pacific Grove, CA
Browner, C. 1995. Policy for Risk Characterization at the US Environmental Protection Agency, Memorandum to assistant and regional administrators
Bumb, R. R., Crummet, W. B., Cutie, S. S., Gledhill, J. R., Hummel, R. H., Kagel, R. O., Lamparski, L. L., Luoma, E. V., Miller, D. L., Nestrick, T. J., Shadoff, L. A., Stehl, R. H., and Woods, J. S. 1980. Trace chemistries of fire: A source of chlorinated dioxins. Science 210 (4468): 385–90CrossRefGoogle ScholarPubMed
Burmaster, D. E., and Thompson, K. M. 1997. Estimating exposure point concentrations for surface soils for use in deterministic and probabilistic risk assessments. Human and Ecological Risk Assessment, 3 (3): 363–84CrossRefGoogle Scholar
Burmaster, D. E., and Thompson, K. M. 1998. Fitting second-order parametric distributions to data using maximum likelihood estimation. Human and Ecological Risk Assessment, 4 (2): 319–39CrossRefGoogle Scholar
Burmaster, D. E., and Wilson, A. M. 1996. An introduction to second-order random variables in human health risk assessment. Human and Ecological Risk Assessment, 2 (4): 892–919CrossRefGoogle Scholar
Cao, H., and Chen, G. 1983. Some applications of fuzzy sets to meteorological forecasting. Fuzzy Sets and Systems, 9: 1–12CrossRefGoogle Scholar
Carrington, C. D. 1996. Logical probability and risk assessment. Human and Ecological Risk Assessment, 2: 62
Carrington, C. D. 1997. An administrative view of model uncertainty in public health. Risk, 8: 273ff. See also:
Cash, D. W. 2000. Distributed assessment systems: An emerging paradigm of research, assessment, and decision-making for environmental change. Global Environmental Change, 10 (4): 241–4CrossRefGoogle Scholar
Casman, E. A., Morgan, M. G., and Dowlatabadi, H. 1999. Mixed levels of uncertainty in complex policy models. Risk Analysis, 19 (1): 33–42CrossRefGoogle Scholar
Chaloner, K. 1996. Elicitation of prior distributions. In D. K. Berry Stangl, ed., Bayesian Biostatistics, pp. 141–56. Marcel Dekker, New York
Charniak, E. 1991. Bayesian networks without tears. AI Magazine, 12 (4): 50–63Google Scholar
Clark, W. C., and Dickson, N. M. 1999. The global environmental assessment project: Learning from efforts to link science and policy in an interdependent world. Acclimations, 8: 6–7. Scholar
Clarke, L. 1988. Politics and bias in risk assessment. The Social Science Journal, 15: 155–65CrossRefGoogle Scholar
Cooke, R. M. 1991. Experts in Uncertainty: Opinion and Subjective Probability in Science. Oxford Press, New York
Costello, C. J., Adams, R. M., and Polasky, S. (1998). The value of El Niño forecasts in the management of salmon: A stochastic dynamic assessment. American Journal of Agricultural Economics, 80: 765–77CrossRefGoogle Scholar
Covello, V. T., and Mumpower, J. 1985. Risk analysis and risk management: A historical perspective. Risk Analysis, 5: 103–20CrossRefGoogle Scholar
Cox, D. C., and Baybutt, P. 1981. Methods for uncertainty analysis: A comparative study. Risk Analysis, 1: 251–8CrossRefGoogle Scholar
CRAM Presidential/Congressional Commission on Risk Assessment and Risk Management. 1997. Risk Assessment and Risk Management in Regulatory Decision Making. Final Report. Washington, DC
Cullen, A. C. 1994. Measures of conservatism in probabilistic risk assessment. Risk Analysis, 14: 389–93CrossRefGoogle ScholarPubMed
Cullen, A. C. 1995. The sensitivity of Monte Carlo simulation results to model assumptions: The case of municipal solid waste combustor risk assessment. Journal of Air Waste Management Association, 45: 538–46CrossRefGoogle Scholar
Cullen, A. C., and Eschenroeder, A. Q. 1997. Coping with muncipal waste. In J. Graham and J. Hartwell eds., The Greening of Industry. Harvard University Press, Cambridge, MA
Cullen, A. C., and Frey, H. C. 1999. Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Plenum Press, New York
Dakins, M. E., Toll, J. E., Small, M. J., and Brand, K. P. 1996. Risk-based environmental remediation: Bayesian Monte Carlo analysis and the expected value of sample information. Risk Analysis, 16 (1): 67–79CrossRefGoogle ScholarPubMed
De Finetti, B. 1937. La Prevision: Ses Lois Logiques, Ses Sources Subjectives. Annales de L'Institut Henri Poincaré, 7 (1). pp. 1–68. English translation in H. Kyburg and H. Smokler, eds. 1964. Studies in Subjective Probability. Wiley, New York
DeGroot, M. H. 1969. Optimal Statistical Decisions, McGraw-Hill Book Company, New York
Dourson, M. L. and Stara, J. F. 1983. Regulatory history and experimental support of uncertainty (safety factors). Regulatory Toxicology and Pharmacology, 3: 224–38CrossRefGoogle Scholar
Draper, D. 1995. Assessment and propagation of model uncertainty. Journal Royal Statistical Society, Series B, 57 (1): 45–97Google Scholar
Eddy, D. M., Hasselblad, V., and Shachter, R. 1990. An introduction to a Bayesian method for meta-analysis: The confidence profile method. Medical Decision Making, 10 (1): 15–23CrossRefGoogle ScholarPubMed
Eisenberg, J. N. S., and McKone, T. E. 1998. Decision tree method for the classification of chemical pollutants: Incorporation of across-chemical variability and within-chemical uncertainty. Environmental Science and Technology, 32 (21): 3396–404CrossRefGoogle Scholar
Evans, J. S. 1985. The value of improved exposure estimates: A decision analytic approach, Proceedings of the 78th Annual Meeting of the Air Pollution Control Association (June 16–21, 1985), Detroit, Michigan
Evans, J. S., Graham, J. D., Gray, G. M., and Sielken, R. L. 1994a. A distributional approach to characterizing low-dose cancer risk. Risk Analysis, 14 (1): 25–34CrossRefGoogle Scholar
Evans, J. S., Gray, G. M., Sielken, R. L., Smith, A. E., Valdez-Flores, C., and Graham, J. D. 1994b. Use of probabilistic expert judgment in distributional analysis of carcinogenic potency, Reg Tox and Pharm, 20: 15–36CrossRefGoogle Scholar
Evans, J., Hawkins, N., and Graham, J. 1988. The value of monitoring for radon in the home: A decision analysis, Journal of the Air Pollution Control Association, 38 (11): 1380–5Google ScholarPubMed
Evans, J. S. 1985. The value of improved exposure estimates: A decision analytic approach, Proceedings of the 78thAnnual Meeting of the Air Pollution Control Association, (June 16–21, 1985), Detroit, Michigan
Ferson, S. 1996. What Monte Carlo methods can not do. Human and Ecological Risk Assessment, 2: 990–1007CrossRefGoogle Scholar
Ferson, S., Ginzburg, L., and Akcakaya, R. Whereof one cannot speak: When input distributions are unknown. Unpublished (apparently) Risk Analysis
Finkel, A. 1990. Confronting Uncertainty in Risk Management: A Guide for Decision Makers. Center for Risk Management, Resources for the Future, Washington, DC
Finkel, A. M., and Evans, J. S. 1987. Evaluating the benefits of uncertainty reduction in environmental health risk management. J Air Pollution Control Association, 37: 1164–71Google ScholarPubMed
Fischhoff, B., Riley, D., Kovacs, D. C., and Small, M. 1998. What information belongs in a warning?Psychology and Marketing, 15 (7): 663–863.0.CO;2-D>CrossRefGoogle Scholar
Fischhoff, B., Slovic, P., and Lichtenstein, S. 1982. Lay foibles and expert fables in judgments about risk. American Statistician, 36: 240–55Google Scholar
Freudenburg, W. 1988. Perceived risk, real risk: Social science and the art of probabilistic risk assessment. Science, 241: 44–9CrossRefGoogle Scholar
Frey, H. C. 1992. Quantitative Analysis of Uncertainty and Variability in Environmental Policy Making. American Association for the Advancement of Science, Washington, DC
Frey, H. C., and Burmaster, D. F. 1999. Methods for characterizing variability and uncertainty: Comparison of bootstrap simulation and likelihood-based approaches. Risk Analysis, 19 (1): 109–30CrossRefGoogle Scholar
Frey, H. C., and Rhodes, D. S. 1996. Characterization and simulation of uncertain frequency distributions: Effects of distribution choice, variability, uncertainty, and parameter dependence. Human and Ecological Risk Assessment, 4 (2): 423–68CrossRefGoogle Scholar
Gee. D. 1997. Criteria for Managing Uncertainty and Regulation in Public Policy. Draft manuscript
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 1995. Bayesian Data Analysis. Chapman & Hall, London
Gentle, J. 1998. Random Number Generation and Monte Carlo Methods. Springer-Verlag, Heidelberg
Giering, E. W., and Kandel, A. 1983. The application of fuzzy set theory to the modeling of competition in ecological systems. Fuzzy Sets and Systems, 9: 103–27CrossRefGoogle Scholar
Goldfarb, T. D. 2000. Environmental Studies. Dushkin McGraw-Hill, Guilford, CT
Good, I. J. 1959. Kinds of probability. Science, 129: 443–7CrossRefGoogle Scholar
Gurian, P. L., Small, M. J., Lockwood III, J. R., and Schervish, M. J. 2001a. Benefit-cost estimation for alternative drinking water maximum contaminant levels. Water Resources Research, 37 (9): 2213–26CrossRefGoogle Scholar
Gurian, P. L., Small, M. J., Lockwood III, J. R., and Schervish, M. J. 2001b. Addressing uncertainty and conflicting cost estimates in revising the arsenic MCL. Environmental Science & Technology, 35 (22): 4414–20CrossRefGoogle Scholar
Haas, C. N. 1999. On modeling correlated random variables in risk assessment. Risk Analysis, 19 (6): 1205–14CrossRefGoogle ScholarPubMed
Habicht, H. 1992. Guidance on Risk Characterization for Risk Managers and Risk Assessors. USEPA memorandum to Assistant and Regional Administrators
Haimes, Y. Y., and Lambert, J. 1994. When and how can you specify a probability distribution when you don't know much, Risk Analysis, 14 (5): 661–706CrossRefGoogle Scholar
Hammitt, J. K., and Shlyakhter, A. I. 1999. The expected value of information and the probability of surprise. Risk Analysis, 19 (1): 135–52CrossRefGoogle Scholar
Henrion, M. 1982. The value of knowing how little you know: The advantages of probabilistic treatment in policy analysis, Ph.D. dissertation, Carnegie-Mellon University, Pittsburgh
Henrion, M., and Fischhoff, B. 1986. Assessing uncertainty in physical constants. American Journal of Physics, 54 (9): 791–8CrossRefGoogle Scholar
Hertwich, E. G., McKone, T. E., and Pease, W. S. 1999. Parameter uncertainty and variability in evaluative fate and exposure models. Risk Analysis, 19: 1193–1204CrossRefGoogle ScholarPubMed
Hilton, R. W. 1981. The determinants of information value: Synthesizing some general results. Management Science, 27 (1): 57–64CrossRefGoogle Scholar
Hoffman, F. O., and Hammonds, J. S. 1994. Propagation of uncertainty in risk assessments: The need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Analysis, 14 (5): 707–12CrossRefGoogle ScholarPubMed
Hunt, S., Frewer, L. J., and Shepard, R. 1999. Public trust in sources of information about radiation risks in the UK. Journal of Risk Research, 2: 167–80CrossRefGoogle Scholar
Iman, R. L., and Conover, W. J. 1982. A distribution-free approach to inducing rank correlation among input variables. Communications in Statistics, B11 (3): 311–34CrossRefGoogle Scholar
Iman, R. L., and Hora, S. C. 1989. Bayesian methods for modeling recovery times with an application to the loss of off-site power at nuclear power plants. Risk Analysis, 9 (1): 25–36CrossRefGoogle Scholar
IPCC (Intergovernmental Panel on Climate Change). 1995. Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses Contribution of Working Group II to the Second Assessment of the Intergovernmental Panel on Climate Change, R. T. Watson, M. C. Zinyowera, and R. H. Moss, eds. Cambridge University Press, Cambridge
IPCC (Intergovernmental Panel on Climate Change). 2001a. Climate Change 2001: The Scientific Basis. See
IPCC (Intergovernmental Panel on Climate Change). 2001b. Climate Change 2001: Impacts, Adaptation and Vulnerability. See
IPCC (Intergovernmental Panel on Climate Change). 2001c. Climate Change 2001: Mitigation. See
Isukapalli, S. S., and Georgopoulos, P. G. 2001. Computational Methods for Sensitivity and Uncertainty Analysis for Environmental and Biological Models. Project Report to U.S. EPA Office of Research and Development (ORD) National Exposure Research Laboratory. EPA/600/R-01-068. Research Triangle Park, NC
Jasanoff, S. 1987. Cultural aspects of risk assessment in Britain and the United States. In B. B. Johnson and V. T. Covello, eds., The Social and Cultural Construction of Risk, pp. 359–97. Reidel, Dordrecht, Netherlands
Jasanoff, S. 1990. The Fifth Branch, Science Advisers as Policymakers. Harvard University Press, Cambridge, MA
Jasanoff, S. 1993. Bridging the two cultures of risk analysis. Risk Analysis, 13: 123–9CrossRefGoogle Scholar
Johnson, B. B., and Slovic, P. 1995. Presenting uncertainty in health risk assessment: Initial studies of its effects on risk perception and trust. Risk Analysis, 15: 485–94CrossRefGoogle ScholarPubMed
Johnson, R., Pankow, J., Bender, D., Price, C., and Zogorski, J. 2000. MTBE: To what extent will past releases contaminate community water supply wells?ES&T, 34 (9): 210A–217AGoogle ScholarPubMed
Julien, B., Fenves, S. J., and Small, M. J. 1992. Knowledge acquisition methods for environmental evaluation. AI Applications, 6: 1–20Google Scholar
Kadane, J. B., and Wolfson, L. J. 1998. Experiences in elicitation. Journal of the Royal Statistical Society, Series D, 47: 3–19 (with discussion)CrossRefGoogle Scholar
Kahneman, D., Slovic, P., and Tversky, A. 1982. Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press, New York
Kalagnanam, J., and Diwekar, U. 1997. An efficient sampling technique for off-line quality control, Technometrics, 38: 308
Kammen, D. M., and Hassenzahl, D. M. 1999. Should We Risk It? Exploring Environmental, Health, and Technological Problem Solving, Princeton University Press, Princeton, NJ
Kasperson, R. E., and Kasperson, J. X. 1996. The social amplification and attenuation of risk. Annals of the American Academy of Political and Social Sciences, 545: 95–105CrossRefGoogle Scholar
Kasperson, R., Renn, O., Slovic, P., Brown, H., Emel, J., Goble, R., Kasperson, J., and Ratick, S. 1988. The social amplification of risk: A conceptual framework. Risk Analysis, 8: 177–87CrossRefGoogle Scholar
Keeney, R. L. 1982. Decision analysis: An overview. Operations Research, 39: 803–38CrossRefGoogle Scholar
Klir, G. J., and Folger, T. A. 1988. Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ
Larkin, L. I. 1985. A fuzzy logic controller for aircraft flight control. In M. Sugeno, ed., Industrial Applications of Fuzzy Control. North-Holland, New York
Lave, L. 1978. “Eight Frameworks of Regulation: The Strategy of Social Regulation,” Brookings Institute, pp. 8–28
Lave, L. B., Resendiz-Carrillo, D., and McMichael, F. C. 1990. Safety goals for high-hazard dams: Are dams too safe?Water Resources Research, 26 (7): 1383–91Google Scholar
Lee, P. M. 1989. Bayesian Statistics: An Introduction, Oxford University Press, Oxford
Lempert, R., Schlesinger, M., and Bankes, S. 1996. When we don't know the costs or the benefits: Adaptive strategies for abating climate change. Climactic Change, 33: 235–74CrossRefGoogle Scholar
Levin, A., Fratt, D. B., Leonard, A., et al.1991. Comparative analysis of health risk assessments for muncipal waste combustors. Journal of the Air and Waste Management Association, 41 (1): 20–31CrossRefGoogle Scholar
Lewis, H. W., Budnitz, R. J., Kouts, H. J. C., Lowenstein, W. B., Rowe, W. D., Von Hippel, F., and Zachariasen, F. 1979. Risk Assessment Review Group Report to theU.S.Nuclear Regulatory Commission. NUREG/CR-0400
Lichtenstein, S., and Fischhoff, B. 1977. Do those who know more also know more about how much they know?Organizational Behavior and Human Performance, 20: 159–83CrossRefGoogle Scholar
Lockwood III, J. R., Schervish, M. J., Gurian, P., and Small, M. H. 2001. Characterization of arsenic occurrence in US drinking water treatment facility source waters. Journal of American Statistical Association, 96: 456, pp. 1184–1193CrossRefGoogle Scholar
MacKenzie, D. 1990. Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance. MIT Press, Cambridge, MA
March, J. G., and Simon, H. A. 1958. Organizations. John Wiley and Sons, New York
McElhany, P., Ruckelshaus, M., Ford, M. J., Wainwright, T., and Bjorkstedt, E. 2000. Viable Salmonid Populations and the Recovery of Evolutionarily Significant Units. National Marine Fisheries Service
McKay, M. D., Conover, W. J., and Beckman, R. J. 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21 (2): 239–45Google Scholar
McKone, T. E., and Bogen, K. T. 1992. Uncertainties in health-risk assessment: An integrated case study based on tetrachloroethylene in California groundwater. Regulatory Toxicology and Pharmacology, 15: 86–103CrossRefGoogle ScholarPubMed
Merkhofer, M. W. 1987. Quantifying judgmental uncertainty: Methodology, experiences, and insights. IEEE Transactions on Systems, Man, and Cybernetics. 17 (5): 741–52CrossRefGoogle Scholar
Merz, J., Small, M. J., and Fischbeck, P. 1992. Measuring decision sensitivity: A combined Monte Carlo-logistic regression approach. Medical Decision Making, 12: 189–96CrossRefGoogle ScholarPubMed
Morgan, M. G., and Dowlatabadi, H. 1996. Learning from integrated assessment of climate change. Climatic Change, 34 (3–4): 337–68CrossRefGoogle Scholar
Morgan, M. G., and Henrion, M. 1990. Uncertainty: A Guide for Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, Cambridge
Morgan, M. G., Fischhoff, B., Bostrom, A., Lave, L., and Atman, C. 1992. Communicating risk to the public. Environmental Science and Technology, 26 (11): 2048–56Google Scholar
Morgan, M. G., Henrion, M., and Morris, S. C. 1980, Expert Judgment for Policy Analysis. BNL 51358. Brookhaven National Laboratory. Brookhaven, NY
Morokoff, W. J., and Caflisch, R. E. 1994. Quasi-random sequences and their discrepancies. SIAMJournal on Scientific Computing, 15 (6): 1251–79CrossRefGoogle Scholar
Nakatsuyama, M., Nagahashi, H., and Nishizuka, N. 1984. Fuzzy logic phase controller for traffic functions in the one-way arterial road. ProceedingIFAC9thTriennial World Congress, pp. 2865–70, Pergamon Press, Oxford
NAS. 1987. Report on the Delaney Paradox. National Academics Press. Washington, DC
NCRP. 1996. A Guide for Uncertainty Analysis in Dose and Risk Assessments Related to Environmental Contamination. National Council on Radiation Protection and Measurements. NCRP Commentary No. 14. Bethesda, MD
Norsys. 1998. Netica™ Application for Belief Networks and Influence Diagrams: User's Guide, Versions 1.03 for Macintosh and 1.05 for Windows. Norsys Software Corporation, Vancouver, BC, Canada
NRC. 1975. Reactor Safety Study: An Assessment of Accident Risks inU.S.Commercial Nuclear Power Plants. WASH-1400/NUREG 751014. U.S. Nuclear Regulatory Commission, Washington, DC
NRC. 1983. Risk Assessment in the Federal Government: Managing the Process (also known as “The Red Book”). National Academy Press, Washington, DC
NRC. 1994. Science and Judgement in Risk Assessment. National Academy Press, Washington, DC
NRC. 1996. Understanding Risk: Informing Decisions in a Democratic Society. National Academy Press, Washington, DC
NRC. 1999. Our Common Journey: A Transition Toward Sustainability, Board on Sustainable Development, National Research Council, National Academy Press, Washington, DC
NRC. 2000. Incorporating Science, Economics, and Sociology in Developing Sanitary and Phytosanitary Standards in International Trade, Proceedings of a Conference. National Research Council, National Academy Press, Washington, DC
Pate, E. 1983. Acceptable decision processes and acceptable risks in public sector regulations. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13: 113–24CrossRefGoogle Scholar
Pate-Cornell, E. M. 1996. Uncertainties in risk analysis: Six levels of treatment. Reliability Engineering and System Safety, 54: 95–111CrossRefGoogle Scholar
Patt, A. 1999. Extreme outcomes: The strategic treatment of low probability events in scientific assessments. Risk, Decision, and Policy, 4 (1): 1–15CrossRefGoogle Scholar
Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufman, San Mateo, CA
Peters, R. G., Covello, V. T., and McCallum, D. B. 1997. The determinants of trust and credibility in environmental risk communication: An empirical study. Risk Analysis, 17: 43–54CrossRefGoogle Scholar
Petts, J. 1998. Trust and waste management information expectation versus observation. Journal of Risk Research, 1: 307–20CrossRefGoogle Scholar
Press, S. J. 1989. Bayesian Statistics: Principles, Models, and Applications. Wiley, New York
Raffensperger, C., and Tickner, J. A., eds. 1999. Protecting Public Health and the Environment – Implementing the Precautionary Principle. Island Press, Washington, DC
Raiffa, H. 1968. Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Addison-Wesley Publishing, Reading, MA
Raiffa, H., and Schlaifer, R.O. 1961. Applied Statistical Decision Theory, Harvard University Press, Cambridge, MA
Ramsey, F. P. 1931. The Foundation of Mathematics and Other Logical Essays. Kegan Paul, London
Renn, O., Burns, W. J., Kasperson, J. X., Kasperson, R. E., and Slovic, P. 1992. The social amplification of risk: Theroetical foundations and empirical applications. Journal of Social Issues, 48: 127–60CrossRefGoogle Scholar
Shlyakhter, A. I. 1994. “Improved Framework for Uncertainty Analysis: Accounting for Unsuspected Errors,” Risk Analysis, 14: 441–7CrossRefGoogle Scholar
Simon, T. W. 1999. Two dimensional Monte Carlo simulation and beyond: A comparison of several probabilistic risk assessment methods applied to a Superfund site. Human and Ecological Risk Assessment, 5 (4): 823–43CrossRefGoogle Scholar
Slovic, P. 1993. Perceived risk, trust, and democracy. Risk Analysis, 13: 675–82CrossRefGoogle Scholar
Slovic, P., Fischoff, B., and Lichtenstein, S. 1979. Rating the risks. Environment, 21: 14–20, 36–9Google Scholar
Small, M. J., and Fischbeck, P. F. 1999. False precision in Bayesian updating with incomplete models. Human and Ecological Risk Assessment, 5 (2): 291–304CrossRefGoogle Scholar
Smith, A. E., Ryan, P. B., and Evans, J. S. 1992. The effect of neglecting correlations when propagating uncertainty and estimating the population distribution of risk. Risk Analysis, 12: 467–74CrossRefGoogle Scholar
Solow, A. R., Adams, R. F., Bryant, K. J., Legler, D. M., O'Brien, J. J., McCarl, B. A., Nayda, W., and Weiher, R. 1998. The value of improved ENSO prediction to U.S. agriculture. Climatic Change, 39: 47–60CrossRefGoogle Scholar
Song, X.-H., and Hopke, P. K. 1996. Solving the chemical mass balance problem using an artificial neural network. Environmental Science & Technology, 30 (2): 531–5CrossRefGoogle Scholar
Spensley, J. W. 1995. National Environmental Policy Act. In T. F. P. Sullivan, ed., Environmental Law Handbook, 13th ed., Chapter 10. Government Institutes, Rockville, MD
Spetzler, C. S., and Holstein, S. 1975. Probability encoding in decision analysis. Management Science, 22 (3): 340–58CrossRefGoogle Scholar
Squillace, P. J., Zogorski, J. S., Wilber, W. G., and Price, C. V. 1996. Preliminary assessment of the occurrence and possible sources of MTBE in groundwater in the United States, 1993–1994. Environmental Science and Technology, 30 (5): 1721–30CrossRefGoogle Scholar
Stiber, N. A., Pantazidou, M., and Small, M. J. 1999. Expert system methodology for evaluating reductive dechlorination at TCE sites. ES&T, 33 (17): 3012–20Google Scholar
Taylor, A. C. 1993. Using objective and subjective information to generate distributions for probabilistic exposure assessment. Journal of Exposure Analysis and Environmental Epidemiology, 3: 285–98Google Scholar
Taylor, A., Evans, J., and McKone, T. 1993. The value of animal test information in environmental control decisions. Risk Analysis, 13: 403–12CrossRefGoogle ScholarPubMed
Tezuka, S. 1995. Uniform Random Numbers: Theory and Practice. Kluwer Academic Publishers, Dordrecht
Thompson, K. M. 1999. Developing univariate distributions from data for risk analysis. Human and Ecological Risk Assessment, 5 (4): 755–83CrossRefGoogle Scholar
Thompson, K. M., and Evans, J. S. 1997. The value of improved exposure information for perchlorethylene (Perc): A case study for dry cleaners. Risk Analysis, 17 (2): 253–71CrossRefGoogle Scholar
Thompson, K. M., and Graham, J. D. 1996. Going beyond the single number: Using probabilistic risk assessment to improve risk management. Human and Ecological Risk Assessment, 2 (4): 1008–34CrossRefGoogle Scholar
Thompson, P. B., and Dean, W. 1996. Competing conceptions of risk. Risk: Health, Safety & Environment, 7: 361–84Google Scholar
Tickner, J. A. 1999. A map toward precautionary decision making. In C. Raffensperger and J. A. Tickner, eds., Protecting Public Health and the Environment – Implementing the Precautionary Principle. Island Press, Washington, DC
UCC Commission for Racial Justice. 1987. Toxic Wastes and Race in the United States. United Church of Christ and Public Data Access, New York
UK-ILGRA. 1996. Use of Risk Assessment within Government Departments. UK Health and Safety Executive, HSE Books, Sudbury, Suffolk, UK. See also
UK-ILGRA. 1999. Risk Assessment and Risk Management – Improving Policy and Practice within Government Departments. UK Health and Safety Executive, HSE Books, Sudbury, Suffolk, UK
U.S. CEQ. 1971. Environmental Quality. GPO, Washington, DC
U.S. EPA Science Advisory Board. 1995. Beyond the Horizon: Using Foresight to Protect the Environmental Future. USEPA Science Advisory Board, Washington, DC
U.S. EPA National Center for Environmental Assessment. 1997a. Exposure Factors Handbook, NTIS: PB98-124217, See also, Washington, DC
U.S. EPA Science Policy Council. 1997b. Proposed Policy for Use of Monte Carlo Analysis in Agency Risk Assessment. Memorandum of William P. Wood, Executive Director, Risk Assessment Forum, to Dorothy E. Patton, Executive Director of Science Policy Council (8104), January 29, 1997
U.S. EPA Risk Assessment Forum. 1998. Report of the Workshop on Selecting Input Distributions for Probabilistic Assessments. Prepared by Eastern Research Group, Inc., for EPA under EPA Contract No. 68-D5-0028, Washington, DC
U.S. EPA Risk Assessment Forum. 1999. Report of the Workshop on Selecting Input Distributions for Probabilistic Assessments USEPA 630/R-98/004. January 1, 1999. U.S. EPA Risk Assessment Forum, Washington, DC
U.S. EPA Office of Research and Development. 2000. Exploratory Research to Anticipate Future Environmental Issues. FY 2000 STAR Program Request for Applications. U.S. EPA ORD National Center for Environmental Research, Washington, DC.
Winkler, R. L. 1967. The assessment of prior distributions in Bayesian analysis. Journal of the American Statistical Association, 62: 776–800CrossRefGoogle Scholar
Wiser, G. 2002. Analysis and perspective: Kyoto Protocol packs powerful implementation punch. International Reporter Current Report, 25 (2): 86. See
Wolfson, L. J. 1995. Elicitation of Priors and Utilities for Bayesian Analysis. PhD Thesis, Department of Statistics, Carnegie Mellon University, Pittsburgh
Wolfson, L. J., Kadane, J. B., and Small, M. J. 1996. Bayesian environmental policy decisions: Two case studies. Ecological Applications, 6 (4): 1056–66CrossRefGoogle Scholar
Wolpert, R. L. 1989. Eliciting and combining subjective judgments about uncertainty. International Journal of Technology Assessment in Health Care, 5 (4): 537–57CrossRefGoogle ScholarPubMed
Wood, E. F., and Rodriguez-Iturbe, I. 1975. Bayesian inference and decision making for extreme hydrologic events. Water Resources Research, 11 (4): 533–42CrossRefGoogle Scholar
Word, C. J., Harding, A. K., Bilyard, G. R., and Weber, J. R. 1999. Basic science and risk communication: A dialogue-based study. Risk: Health Safety and Environment, 10 (2), 231–42Google Scholar
World Watch Institute. 2000. State of the World. Norton, New York
Wynne, B. 1980. Technology, risk and participation: The social treatment of uncertainty. In J. Conrad, ed., Society, Technology and Risk, pp. 83–107. Academic Press, New York
Wynne, B. 1987. Risk Management and Hazardous Wastes: Implementation and the Dialectics of Credibility. Springer, Berlin
Bedford, T., and Cooke, R. 2001. Probabilistic Risk Analysis: Foundations and Methods, Cambridge University Press, Cambridge
Covello, V. T., and Merkhofer, M. W. 1993. Risk Assessment Methods: Approaches for Assessing Health and Environmental Risks. Plenum Press, New York
Crawford-Brown, D. J. 1999. Risk-Based Environmental Decisions: Methods and Culture. Kluwer Academic Publishers, Boston
Cullen, A. C., and Frey, H. C. 1999. Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing With Variability and Uncertainty in Models and Inputs. Plenum Press, New York
Jaeger, C. C., Renn, O., Rosa, E. A., and Webler, T. 2001. Rational Action, Risk, and Uncertainty. Earthscan, London
Kadane, J. B., and Wolfson, L. J. 1998. Experiences in elicitation. Journal of the Royal Statistical Society D, 47: 3–19 (with discussion)CrossRefGoogle Scholar
Kammen, D. M., and Hassenzahl, D. M. 1999. Should We Risk It? Exploring Environmental, Health, and Technological Problem Solving, Princeton University Press, Princeton, NJ
Klapp, M. G. 1992. Bargaining With Uncertainty: Decision-Making in Public Health, Technologial Safety, and Environmental Quality. Auburn House, Dover, MA
Morgan, M. G., and Henrion, M. 1990. Uncertainty: A Guide for Dealing with Uncertainty in Quantitative Risk and Policy Analysis, Cambridge University Press, Cambridge
Porter, T. M. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, Princeton University Press, Princeton, NJ
Slovic, P. 2000. The Perception of Risk. Earthscan, London
Stirling, A. 1999. On Science and Precaution in the Management of Technological Risk: Volume I. A Synthesis Report of Case Studies. Report EUR 19056/EN. European Commission for Prospective Technological Studies, Seville, Spain
Stirling, A. 2002. On Science and Precaution in the Management of Technological Risk: Volume II. Case Studies. Report EUR 19056/EN/2. European Commission for Prospective Technological Studies, Seville, Spain
Warren-Hicks, W. H., and Moore, D. R. J., eds. 1998. Uncertainty Analysis in Ecological Risk Assessment. Society of Environmental Toxicology and Chemistry (SETAC), Pensacola, FL

Save book to Kindle

To save this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats