TY - JOUR
T1 - The Future of Sensitivity Analysis
T2 - An essential discipline for systems modeling and policy support
AU - Razavi, Saman
AU - Jakeman, Anthony
AU - Saltelli, Andrea
AU - Prieur, Clémentine
AU - Iooss, Bertrand
AU - Borgonovo, Emanuele
AU - Plischke, Elmar
AU - Lo Piano, Samuele
AU - Iwanaga, Takuya
AU - Becker, William
AU - Tarantola, Stefano
AU - Guillaume, Joseph H.A.
AU - Jakeman, John
AU - Gupta, Hoshin
AU - Melillo, Nicola
AU - Rabitti, Giovanni
AU - Chabridon, Vincent
AU - Duan, Qingyun
AU - Sun, Xifu
AU - Smith, Stefán
AU - Sheikholeslami, Razi
AU - Hosseini, Nasim
AU - Asadzadeh, Masoud
AU - Puy, Arnald
AU - Kucherenko, Sergei
AU - Maier, Holger R.
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Decision making
KW - Machine learning
KW - Mathematical modeling
KW - Model robustness
KW - Model validation and verification
KW - Policy support
KW - Sensitivity analysis
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85099475423&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2020.104954
DO - 10.1016/j.envsoft.2020.104954
M3 - Article
SN - 1364-8152
VL - 137
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 104954
ER -