Uncovering Heterogeneous Effects in Computational Models for Sustainable Decision-Making

Computational modeling is frequently incorporated into environmental decision-making in order to capture inherently complex relationships and system dynamics. The complexity of such models often lies in various heterogeneous effects that arise due to the interaction of different input factors or due to designed structural variation in the model. In the past, various sensitivity analysis approaches have been implemented in attempts to identify essential decision factors. However, existing sensitivity analysis methods fail to capture critical information in the presence of heterogeneous effects. In this paper, the recently introduced simulation decomposition (SimDec) visualization method is extended to include quantitative sensitivity analysis. The framework is tested on several decision-making problems and is shown to capture heterogeneous behavior. A formal definition and classification of heterogeneous effects for computational models is introduced. The framework is open-sourced in a variety of scientific programming languages.