1. European Commission http://www.jrc.cec.eu.int/uasa1 DG Joint Research Centre Formal and informal approaches to the quality of information in integrated assessment Stefano Tarantola January 24-25, 2002 Laxenburg, Austria

2. European Commission http://www.jrc.cec.eu.int/uasa2 Information used as input to policy-making is complex, uncertain and disputed. Established guidelines: eg NUSAP and model Pedigree schemes for the quality assurance of the decision process. Tools for Extended quality assurance Silvio.funtowicz@jrc.it

3. European Commission http://www.jrc.cec.eu.int/uasa3 To set the frame [Leamer, 1990 ] (economist): “I propose a form of organised sensitivity analysis in which a neighborhood of alternative assumptions is selected and the corresponding interval of inferences is identified. Chair in Man. Bus. Econ., UCLA

4. European Commission http://www.jrc.cec.eu.int/uasa4 To set the frame “Conclusions are judged to be sturdy only if the neighborhood of assumptions is wide enough to be credible and the corresponding interval of inferences is narrow enough to be useful.” Edward E. Leamer, 1990 “Sensitivity Analysis would help”, in Modelling Economic Series, Edited by CWJ Granger, Clarendon Press, Oxford. Chair in Man. Bus. Econ., UCLA

5. European Commission http://www.jrc.cec.eu.int/uasa5 To set the frame We move one step further: after characterising the interval of inferences (using e.g. the statistical variance), Input factors should be interpreted in sensu lato: - alternative assumptions, - choice of model, - algorithmic alternatives, - poorly-known data... Decomposition of model prediction uncertainty we apportion such variability to its constituents (the input factors) in the space of the assumptions (or input space).

6. European Commission http://www.jrc.cec.eu.int/uasa6 The Case Study: incineration vs. landfill (Austria 1994) No defensible choice is possible given the uncertainties. Robustness assessment fails: the interval of the inference is too wide Y

7. European Commission http://www.jrc.cec.eu.int/uasa7 The Case Study: incineration vs. landfill (Austria 1994) B A

8. European Commission http://www.jrc.cec.eu.int/uasa8 Settings for the sensitivity analysis To validate or invalidate assessments GSA used to show that the uncertainty in the decision on whether to burn or dispose solid waste depends on the choice of the system of indicators, and not on the quality of the available data. Money should not be spent to improve quality in data, but to reach a consensus on the proper system of indicators. V(Y)=V[E(Y|X i )]+E[V(Y|X i )] Tarantola et al., in Saltelli et al. Eds, (2000) Sensitivity Analysis John Wiley

9. European Commission http://www.jrc.cec.eu.int/uasa9 Settings for the sensitivity analysis Useless discussing about the use of different architectures to build the composite indicator, when these do not influence the result. Problem simplification and dialogue optimisation We look for those uncertain factors that have negligible influence on the output. These can be fixed to the most plausible value within their range of variation. The dimensionality of the input space is then reduced.

10. European Commission http://www.jrc.cec.eu.int/uasa10 Settings for the sensitivity analysis Output uncertainty reduction Joint use of UA and GSA (iterative procedure). Perform UA and get a confidence interval for the output If it is unacceptably large, acquire better knowledge on the most important factors. Perform UA again to check... It the output quality exceeds the requirements, the specifications on the input quality can be relaxed, starting from the less important factors. Crosetto and Tarantola (2001) Int J Geogr Inf Science

11. European Commission http://www.jrc.cec.eu.int/uasa11 Bibliography [1] Saltelli, A., K. Chan, M. Scott, Editors, 2000, Sensitivity analysis, John Wiley & Sons publishers, Probability and Statistics series. [2] Saltelli, A., Chan, K., Scott, M. Eds., 1999, Special Issue on sensitivity analysis, Computer Physics Communications, 117. [3] Saltelli A., Tarantola S., and Chan K., 1999, A quantitative, model independent method for global sensitivity analysis of model output, Technometrics, 41(1), 39-56. [4] Saltelli A., Tarantola S., Campolongo F., 2001, Sensitivity analysis as an ingredient of modelling, Statistical Science, 15(4), 377-395.