Practices for global sensitivity analysis of model output are described in a recent textbook (Saltelli et al., 2007). These include (i) variance based techniques for general use in modelling, (ii) the elementary effect method for factor screening for factors-rich models and (iii) Monte Carlo filtering. In the present work we try to put the practices into the context of their usage. We start by describing the present debate on the use of scientific models, and how uncertainty and sensitivity analysis can assist is testing model quality. We discuss Type I, II and III errors in the context of sensitivity analysis and what are the requirements for a good analysis. We also present sensitivity analysis in relation to post normal science (PNS) and model pedigrees.
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References
Baudrillard, J., 1999, Revenge of the Crystal, Pluto Press, London, UK, p. 92.
Beck, M. B., Ravetz, J. R., Mulkey, L. A., and Barnwell, T. O., 1997, Stochastic Hydrology and Hydraulics, 11, 229–254.
Beven, K. J., 2001, Rainfall-Runoff Modelling: The Primer. John Wiley and Sons, Ltd, Chichester. See also (31), pp. 151–192, 2004.
Chatfield, C., 1993, Model uncertainty, data mining and statistical inference, Journal of the Royal Statistical Society A, 158(3), 419–466.
Crichton, M., 2004, State of Fear, Harper Collins, New York, New York.
Dunn, W. N., 1997, Cognitive Impairment and Social Problem Solving: Some Tests for Type III Errors in Policy Analysis, Graduate School of Public and International Affairs, University of Pittsburgh, Pittsburgh.
European Commission SEC, 2005, 791 IMPACT ASSESSMENT GUIDELINES, 15 June 2005, http://ec.europa.eu/governance/docs/index en.htm
Environmental Protection Agency, Models Guidance Draft — November 2003 Draft Guidance on the Development, Evaluation, and Application of Regulatory Environmental Models Prepared by: The Council for Regulatory Environmental Modeling, http://cfpub.epa.gov/crem/cremlib.cfm.
Funtowicz, S. O. and Ravetz, J. R., 1990, Uncertainty and Quality in Science for Policy, Kluwer Academic, Dordrecht, NL.
Funtowicz, S. O. and Ravetz, J. R., 1993, Science for the post-normal age. Futures, 25, 735 755.
Funtowicz, S., O'Connor, M., Faucheux, S., Froger, G., and Munda, G., 1996, Emergent complexity and procedural rationality: post-normal science for sustainability, In: Costanza R, Segura O, Martinez-Alier J, editors. Getting down to earth: practical applications of ecological economics. Washington (D.C.): Island Press, 1996:223–48.
Hoeting, J. A., Madigan, D., Raftery A. E., and Volinsky C. T., 1999, Bayesian model averaging: a tutorial, Statistical Science, 14(4), 382417.
Kennedy, P., 2007, A Guide to Econometrics, Fifth edition, Blackwell, Oxford, UK.
Konikov, L. F. and Bredehoeft, J. D., 1992, Groundwater models cannot be validated, Advances in Water Resources, 15(1), 75–83.
Leamer, E. E., 1990, “Let's take the con out of econometrics” and “sensitivity analysis would help”, in Modelling Economic Series (ed. Granger, C. W. J.), Clarendon, Oxford.
Macilwain, C., 2006, Safe and sound? Nature, 442, 242–243 (20 July 2006).
Michaels, D., 2005, Doubt is their product, Scientiffc American, 292(6), 96–101.
Office of Management and Budget (OMB), 2002, Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by Federal Agencies; Notice; Republication, http://www.whitehouse.gov/omb/inforeg/, February 22, 2002.
Office of Management and Budget (OMB), 2006, Proposed Risk Assessment Bulletin (January 9, 2006), http://www.whitehouse.gov/omb/inforeg/
Oreskes, N., Shrader-Frechette, K., and Belitz, K., 1994, Verification, validation, and confirmation of numerical models in the Earth sciences, Science, 263, 641–646.
Pilkey, O. and Pilkey-Jarvis, L., 2007, Useless Arithmetic: Why Environmental Scientists Can't Predict the Future, Columbia University Press, Irvington, New York.
Rosen, R., 1991, Life Itself – A Comprehensive Inquiry into Nature, Origin, and Fabrication of Life. Columbia University Press, Irvington, New York.
Saltelli, A., Andres, T., Campolongo, F., Cariboni J., Gatelli D., Ratto, M., Saisana, M., and Tarantola, S., 2007, Sensitivity Analysis of Scientific Models, John Wiley and Sons, to appear in winter 2007. (forthcoming)
Saltelli, K., Chan, A., and Scott, M., Editors, 2000, Sensitivity Analysis, Probability and Statistics series, John Wiley and Sons. (forthcoming)
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M., 2004, Sensitivity Analysis in Practice. A Guide to Assessing Scientific Models, John Wiley and Sons, Chichester, New York.
Taleb, N., 2007, The Black Swan, Penguin, London.
Van der Sluijs, J. P., 2002, A way out of the credibility crisis of models used in integrated environmental assessment, Futures, 34, 133–146.
Van der Sluijs, J. P., Craye, M., Funtowicz, S., Kloprogge, P., Ravetz, J., and Risbey, J., 2005, Combining quantitative and qualitative measures of uncertainty in model-based environmental assessment: the NUSAP system, Risk Analysis, 25(2), 481–492.
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Saltelli, A., Vidoni, D., Mascherini, M. (2009). Recommended practices in global sensitivity analysis. In: Baveye, P.C., Laba, M., Mysiak, J. (eds) Uncertainties in Environmental Modelling and Consequences for Policy Making. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2636-1_8
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