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http://hdl.handle.net/10609/114686
Title: Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices
Author: Saltelli, Andrea
Aleksankina, Ksenia
Becker, William
Fennell, Pamela
Ferretti, Federico
Holst, Niels
Li, Sushan
Wu, Qiongli
Keywords: sensitivity analysis
methods
good practices
Issue Date: 2-Apr-2019
Publisher: Environmental Modelling & Software
Citation: Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S. & Wu, Q. (2019). Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental Modelling & Software, 114(), 29-39. doi: 10.1016/j.envsoft.2019.01.012
Published in: 114;
Also see: https://www.sciencedirect.com/science/article/pii/S1364815218302822
Abstract: Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question 'How uncertain is the prediction?' Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their range of existence, and this is equally true of a sensitivity analysis. Despite this, many uncertainty and sensitivity analyses still explore the input space moving along one-dimensional corridors leaving space of the input factors mostly unexplored. Our extensive systematic literature review shows that many highly cited papers (42% in the present analysis) fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and recognized good practices. We end by exploring possible reasons for this problem, and suggest some guidelines for proper use of the methods.
Language: English
URI: http://hdl.handle.net/10609/114686
ISSN: 1364-8152MIAR
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