The future of sensitivity analysis: An essential discipline for systems modeling and policy support


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.

Publication Type
Journal Article
Saman Razavi, University of Saskatchewan
Tony Jakeman, Australian National University
Andrea Saltelli, Open University of Catalonia
Clémentine Prieur
Bertrand Iooss
Emanuele Borgonovo
Elmar Plischke
Samuele Lo Piano
Takuya Iwanaga, Australian National University
William Becker
Stefano Tarantola
Joseph H.A. Guillaume
John Jakeman
Hoshin Gupta
Nicola Melillo
Giovanni Rabitti
Vincent Chabridon
Qingyun Duan
Xifu Sun
Stefán Smith
Environmental Modelling & Software

Related Content