Structural dominance analysis of large and stochastic models

The last decade and a half has seen significant efforts to develop and automate methods for identifying structural dominance in system dynamics models. To date, however, the interpretation and testing of these methods have been with small deterministic models (fewer than five stocks) that show smooth behavioral transitions. While the analysis of simple and stable models is an obvious first step in providing proof of concept, the methods have become stable enough to be tested on a wider range of models. In this paper I report the findings from expanding the application domain of these methods in two important dimensions: increasing model size and incorporating stochastic variance in some model variables. I find that the methods work as predicted with large stochastic models, that they generate insights that are consistent with the existing explanations for the behavior of the tested model, and that they do so in an efficient way. Copyright (c) 2016 System Dynamics Society