Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model

Abstract

Variance-based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance-based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance-based sensitivity analysis to the model allows us to test for non-additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.