Is VARS more intuitive and efficient than Sobol' indices?. (March 2021)

• Record Type: Journal Article
• Authors: Puy, Arnald; Lo Piano, Samuele; Saltelli, Andrea
• Is Part Of: Environmental modelling & software. Volume 137(2021)
• Publication Date: 2021-03
• Subjects: Uncertainty -- Sensitivity analysis -- Modeling -- Statistics -- Design of experiment; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.104960
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The Variogram Analysis of Response Surfaces (VARS) has been proposed by Razavi and Gupta as a new comprehensive framework in sensitivity analysis. According to these authors, VARS provides a more intuitive notion of sensitivity and is much more computationally efficient than Sobol' indices. Here we review these arguments and critically compare the performance of VARS-TO, for total-order index, against the total-order Jansen estimator. We argue that, unlike classic variance-based methods, VARS lacks a clear definition of what an "important" factor is, and we show that the alleged computational superiority of VARS does not withstand scrutiny. We conclude that while VARS enriches the spectrum of existing methods for sensitivity analysis, especially for a diagnostic use of mathematical models, it complements rather than replaces classic estimators used in variance-based sensitivity analysis. Highlights: Razavi and Gupta suggest that VARS is more intuitive and efficient than Sobol' indices. We review these arguments through literature survey and computer simulations. We conclude that these claims do not find empirical support. VARS enriches rather than substitutes variance-based sensitivity analysis.