The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Saman Razavi, Anthony Jakeman, Andrea Saltelli, Clémentine Prieur, Bertrand Iooss, Emanuele Borgonovo, Elmar Plischke, Samuele Lo Piano, Takuya Iwanaga, William Becker, Stefano Tarantola, Joseph H.A. Guillaume, John Jakeman, Hoshin Gupta, Nicola Melillo, Giovanni Rabitti, Vincent Chabridon, Qingyun Duan, Xifu Sun, Stefán SmithRazi Sheikholeslami, Nasim Hosseini, Masoud Asadzadeh, Arnald Puy, Sergei Kucherenko, Holger R. Maier

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number104954
JournalEnvironmental Modelling and Software
Volume137
DOIs
StatePublished - Mar 2021

Keywords

  • Decision making
  • Machine learning
  • Mathematical modeling
  • Model robustness
  • Model validation and verification
  • Policy support
  • Sensitivity analysis
  • Uncertainty quantification

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

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