Guidance framework and software for understanding and achieving system robustness. (August 2021)

• Record Type: Journal Article
• Authors: McPhail, C.; Maier, H.R.; Westra, S.; van der Linden, L.; Kwakkel, J.H.
• Is Part Of: Environmental modelling & software. Volume 142(2021)
• Publication Date: 2021-08
• Subjects: Deep uncertainty -- Decision making under uncertainty -- Robustness -- Scenarios; 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.2021.105059
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: To aid decision making about environmental systems under deep uncertainty, robustness metrics are commonly used to represent system performance over a number of scenarios. However, there are many robustness metrics and many ways of generating scenarios, making it difficult to know which to choose in order to quantify system robustness and to make robust decisions. To address this shortcoming, we introduce a generic guidance framework to assist with the identification of the most robust decision alternatives, as well as the RAPID (Robustness Analysis Producing Intelligent Decisions) software package which is a consistent and easy-to-use implementation of the framework. We illustrate the framework and software package on a hypothetical lake pollution problem, known as The Lake Problem, showing how the framework and software package apply to several situations where decision-makers may or may not know which scenarios or robustness metrics to use. Highlights: Robustness is used to measure system performance under deep uncertainty. Robustness values depend on the choice of robustness metrics and scenarios. We introduce a guidance framework for selecting these metrics and scenarios. The framework is implemented in the RAPID software to simplify robustness analysis. The framework and software are illustrated on the Lake Problem.