The catalogue of methodologies for uncertainty analysis and decision tree presented here provide a guide to how to make an assessment of uncertainties in different types of flood risk management applications. The most important question at this point is whether taking account of uncertainties will change the nature of the decision that is made. The answer is undoubtedly yes. The way in which uncertainty might affect a decision, however, will depend on the context of a decision. In some forms of risk assessment (such as in the nuclear industry) the context has been primarily within the context of the science and engineering of the industry itself, and attempts have been made to quantify the different sources of uncertainty in purely probabilistic terms. Formal risk based decision making frameworks can be used when it is accepted that all uncertainties can be represented probabilistically (e.g. Bedford and Cooke, 2001).

In other environmental modelling problems the context of a decision must be drawn more widely to include more qualitative and epistemic uncertainties and a wider range of stakeholders. This issue has been at the heart of critiques of science-based environmental decision making from a cultural and social perspective (e.g. Funtowicz and Ravetz, 1990, 1999; Pielke, 1999; van Asselt and Rotmans, 2002; Pahl-Wostl, 2002; Pielke and Conant, 2003). The argument is that the normal scientific context cannot take account of all the uncertainties involved in environmental decision making, particularly when decisions will have impacts on stakeholders, including the general public, beyond the set of scientists evaluating the risks. Given that in many cases the interests of stakeholders will be in conflict, then different forms of decision making frameworks may be appropriate.

The representation of severe uncertainties in decision making is presents particular challenges. Conventional Bayesian decision theory (Raiffa, 1968, French, 1986, Lindley, 1990) requires that all uncertainties are represented in probabilistic terms, and, furthermore, that decision makers are able to represent their attitudes to uncertainties in the form of utility functions. These requirements have been widely challenged (Keynes, 1921, Levi, 1982, Ben-Haim, 2001), particularly in situations of severe epistemic uncertainties. Where probability distributions cannot be precisely defined it may be more attractive to proceed with decision making on the basis of bounds on sets of probability distributions (Walley 1991) or seek options that perform acceptably well under a wide range of plausible conditions (Lempert et al 2003). Ben-Haim's info-gap theory (Ben-Haim 2001) falls into this latter class of methods for robust decision making.

Methods

• Info-gap theory

• Bayesian decision trees

• Risk-based utility theory

References

• Bedford, T and Cooke, R, 2001, Probabilistic Risk Analysis: Foundations and Methods, Cambridge University Press.

• Ben-Haim, Y. 2001 Information-Gap Decision Theory: Decisions Under Severe Uncertainty. Academic Press, San Diego.

• Ben-Haim, Y, 2006, Info-Gap Decision Theory, 2nd Edition, Academic Press: Amsterdam.

• French, S. 1986 Decision Theory: An Introduction to the Mathematics of Rationality. Ellis Horwood, Chichester.

• Funtowicz, S O and Ravetz, J, 1990, Uncertainty and quality in science for policy, Kluwer Academic: Dordrecht.

• Funtowicz, S O and Ravetz, J, 1999, Post-normal science: an insight now maturing, Futures, 25: 735-755.

• Keynes, J.M. 1921 A Treatise on Probability. MacMillan?, London.

• Lempert, R. J., Popper, S. W. and Bankes, S. C., 2003, Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis, RAND, Santa Monica, CA.

• Levi, I. 1982 Ignorance, probability and rational choice. Synthese, 53, 387-417.

• Lindley, D.V. 1990 Making Decisions. 2nd ed. Wiley, London.

• Pahl-Wostl, C, 2002, Towards sustainability in the water sector: the importance of human actors and processes of social learning. Aquatic Sciences, 64: 394-411.

• Pielke, R A, Jr., 1999, Who decides? Forecasts and responsibilities in the 1997 Red River Floods, Appl. Behav. Sci. Rev., 7:83-101.

• Pielke, R A, Jr and Conant, R T, 2003, Best practices in prediction for decision-making: lessons from the atmospheric and earth sciences, Ecology, 84(6): 1351-1358.

• Raiffa, H. 1968 Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Addison-Wesley, Reading MA.

• Van Asselt, M B A, and Rotmans, J, 2002, Uncertainty in integrated assessment modelling, from Positivism to Pleuralism, Clmate Change, 54: 75-105.

• Walley, P. 1991 Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London.