Authors: Andrea Saltelli; Ângela Guimarães Pereira; Jeroen P. Van der Sluijs; Silvio Funtowicz
Addresses: European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, Via E. Fermi, 2749, 21027 Ispra (VA), Italy ' European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, Via E. Fermi, 2749, 21027 Ispra (VA), Italy ' Copernicus Institute of Sustainable Development, Utrecht University (NL), Heidelberglaan 2, 3584 CS Utrecht, The Netherlands ' Centre for the Study of the Sciences and the Humanities (SVT) Allegaten, University of Bergen (NO), 34 – Postboks 7805 5020 Bergen, Norway
Abstract: Sensitivity analysis, mandated by existing guidelines as a good practice to use in conjunction to mathematical modelling, is as such insufficient to ensure quality in the treatment of uncertainty of science for policy. If one accepts that policy-related science calls for an extension of the traditional internal, peer review-based methods of quality assurance to higher levels of supervision, where extended participation and explicit value judgments are necessary, then by the same token sensitivity analysis must extend beyond the technical exploration of the space of uncertain assumptions when the inference being sought via mathematical modelling is subject to relevant uncertainties and stakes. We thus provide seven rules to extend the use of sensitivity analysis (or how to apportion uncertainty in model-based inference among input factors) in a process of sensitivity auditing of models used in a policy context. Each rule will be illustrated by examples.
Keywords: mathematical modelling; sensitivity analysis; sensitivity auditing; NUSAP; post normal science; PNS; knowledge quality assessment; uncertainty; model-based inference; policy.
International Journal of Foresight and Innovation Policy, 2013 Vol.9 No.2/3/4, pp.213 - 234
Received: 05 Jan 2013
Accepted: 25 Sep 2013
Published online: 10 Jan 2014 *