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DG Joint Research Centre

DG Joint Research Centre. Formal and informal approaches to the quality of information in integrated assessment Stefano Tarantola January 24-25, 2002 Laxenburg, Austria. Tools for Extended quality assurance. Information used as input to policy-making is complex, uncertain and disputed.

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DG Joint Research Centre

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

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

  3. Chair in Man. Bus. Econ., UCLA Alternative frameworks Models Indicators Space of the assumptions Space of the inferences 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. http://www.jrc.cec.eu.int/uasa

  4. Alternative frameworks Space of the assumptions 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 http://www.jrc.cec.eu.int/uasa

  5. we apportion such variability to its constituents (the input factors) in the space of the assumptions (or input space). Decomposition of model prediction uncertainty 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... http://www.jrc.cec.eu.int/uasa

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

  7. The Case Study: incineration vs. landfill (Austria 1994) A B Space of the assumptions Output uncertainty http://www.jrc.cec.eu.int/uasa

  8. 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. Tarantola et al., in Saltelli et al. Eds, (2000)Sensitivity Analysis John Wiley V(Y)=V[E(Y|Xi)]+E[V(Y|Xi)] http://www.jrc.cec.eu.int/uasa

  9. Settings for the sensitivity analysis 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. Useless discussing about the use of different architectures to build the composite indicator, when these do not influence the result. http://www.jrc.cec.eu.int/uasa

  10. 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 http://www.jrc.cec.eu.int/uasa

  11. 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. http://www.jrc.cec.eu.int/uasa

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