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                    Section 1: Publication
                                
                Publication Type
                Journal Article
                                
                Authorship
                Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., ... & Maier, H. R. 
                                
                Title
                The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
                                
                Year
                2021
                                
                Publication Outlet
                Environmental Modelling & Software, 137, 104954. 
                                
                DOI
                
                                
                ISBN
                
                                
                ISSN
                
                                
                Citation
                
                    Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., ... & Maier, H. R. (2021). The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environmental Modelling & Software, 137, 104954. 
https://doi.org/10.1016/j.envsoft.2020.104954
                 
                                
                Abstract
                
                    Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.
                
                                
                Plain Language Summary