@article{Razavi-2021-The,
title = "The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support",
author = "Razavi, Saman and
Jakeman, Anthony J. and
Saltelli, Andrea and
Prieur, Cl{\'e}mentine and
Iooss, Bertrand and
Borgonovo, Emanuele and
Plischke, Elmar and
Piano, Samuele Lo and
Iwanaga, Takuya and
Becker, William E. and
Tarantola, Stefano and
Guillaume, Joseph H. A. and
Jakeman, John and
Gupta, Hoshin V. and
Melillo, Nicola and
Rabitti, Giovanni and
Chabridon, Vincent and
Duan, Qingyun and
Sun, Xifu and
Smith, Stef{\'a}n Thor and
Sheikholeslami, Razi and
Hosseini, Nasim and
Asadzadeh, Masoud and
Puy, Arnald and
Kucherenko, Sergei and
Maier, Holger R. and
Razavi, Saman and
Jakeman, Anthony J. and
Saltelli, Andrea and
Prieur, Cl{\'e}mentine and
Iooss, Bertrand and
Borgonovo, Emanuele and
Plischke, Elmar and
Piano, Samuele Lo and
Iwanaga, Takuya and
Becker, William E. and
Tarantola, Stefano and
Guillaume, Joseph H. A. and
Jakeman, John and
Gupta, Hoshin V. and
Melillo, Nicola and
Rabitti, Giovanni and
Chabridon, Vincent and
Duan, Qingyun and
Sun, Xifu and
Smith, Stef{\'a}n Thor and
Sheikholeslami, Razi and
Hosseini, Nasim and
Asadzadeh, Masoud and
Puy, Arnald and
Kucherenko, Sergei and
Maier, Holger R.",
journal = "Environmental Modelling {\&} Software, Volume 137",
volume = "137",
year = "2021",
publisher = "Elsevier BV",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G21-147001",
doi = "10.1016/j.envsoft.2020.104954",
pages = "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. {\mbox{$\bullet$}} Sensitivity analysis (SA) should be promoted as an independent discipline. {\mbox{$\bullet$}} Several grand challenges hinder full realization of the benefits of SA. {\mbox{$\bullet$}} The potential of SA for systems modeling {\&} machine learning is untapped. {\mbox{$\bullet$}} New prospects exist for SA to support uncertainty quantification {\&} decision making. {\mbox{$\bullet$}} Coordination rather than consensus is key to cross-fertilize new ideas.",
}
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<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. \bullet Sensitivity analysis (SA) should be promoted as an independent discipline. \bullet Several grand challenges hinder full realization of the benefits of SA. \bullet The potential of SA for systems modeling & machine learning is untapped. \bullet New prospects exist for SA to support uncertainty quantification & decision making. \bullet Coordination rather than consensus is key to cross-fertilize new ideas.</abstract>
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%0 Journal Article
%T The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
%A Razavi, Saman
%A Jakeman, Anthony J.
%A Saltelli, Andrea
%A Prieur, Clémentine
%A Iooss, Bertrand
%A Borgonovo, Emanuele
%A Plischke, Elmar
%A Piano, Samuele Lo
%A Iwanaga, Takuya
%A Becker, William E.
%A Tarantola, Stefano
%A Guillaume, Joseph H. A.
%A Jakeman, John
%A Gupta, Hoshin V.
%A Melillo, Nicola
%A Rabitti, Giovanni
%A Chabridon, Vincent
%A Duan, Qingyun
%A Sun, Xifu
%A Smith, Stefán Thor
%A Sheikholeslami, Razi
%A Hosseini, Nasim
%A Asadzadeh, Masoud
%A Puy, Arnald
%A Kucherenko, Sergei
%A Maier, Holger R.
%J Environmental Modelling & Software, Volume 137
%D 2021
%V 137
%I Elsevier BV
%F Razavi-2021-The
%X 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. \bullet Sensitivity analysis (SA) should be promoted as an independent discipline. \bullet Several grand challenges hinder full realization of the benefits of SA. \bullet The potential of SA for systems modeling & machine learning is untapped. \bullet New prospects exist for SA to support uncertainty quantification & decision making. \bullet Coordination rather than consensus is key to cross-fertilize new ideas.
%R 10.1016/j.envsoft.2020.104954
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-147001
%U https://doi.org/10.1016/j.envsoft.2020.104954
%P 104954
Markdown (Informal)
[The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support](https://gwf-uwaterloo.github.io/gwf-publications/G21-147001) (Razavi et al., GWF 2021)
ACL
- Saman Razavi, Anthony J. Jakeman, Andrea Saltelli, Clémentine Prieur, Bertrand Iooss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William E. Becker, Stefano Tarantola, Joseph H. A. Guillaume, John Jakeman, Hoshin V. Gupta, Nicola Melillo, Giovanni Rabitti, Vincent Chabridon, Qingyun Duan, Xifu Sun, et al.. 2021. The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environmental Modelling & Software, Volume 137, 137:104954.
