Strategic model reduction by analysing model sloppiness: A case study in coral calcification. (January 2023)

• Record Type: Journal Article
• Authors: Vollert, Sarah A.; Drovandi, Christopher; Monsalve-Bravo, Gloria M.; Adams, Matthew P.
• Is Part Of: Environmental modelling & software. Volume 159(2023)
• Publication Date: 2023-01
• Subjects: Sensitivity analysis -- Bayesian inference -- Sequential Monte Carlo -- Maximum likelihood estimation -- Parameter interdependence -- Model reduction; 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.2022.105578
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: It can be difficult to identify ways to reduce the complexity of large models whilst maintaining predictive power, particularly where there are hidden parameter interdependencies. Here, we demonstrate that the analysis of model sloppiness can be a new invaluable tool for strategically simplifying complex models. Such an analysis identifies parameter combinations which strongly and/or weakly inform model behaviours, yet the approach has not previously been used to inform model reduction. Using a case study on a coral calcification model calibrated to experimental data, we show how the analysis of model sloppiness can strategically inform model simplifications which maintain predictive power. Additionally, when comparing various approaches to analysing sloppiness, we find that Bayesian methods can be advantageous when unambiguous identification of the best–fit model parameters is a challenge for standard optimisation procedures. Highlights: We propose a new model reduction framework based on a sloppy analysis. Model reduction is guided by identifying mechanisms which weakly inform predictions. We successfully reduce a complex coral growth model, preserving predictive accuracy. A Bayesian sloppy analysis is advantageous when the likelihood surface is complex.