Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model
Abstract
Variance-based sensitivity methods can provide insights into large computational models. We present a novel application of sensitivity analysis to the Biomass Scenario Model (BSM) a large and complex system dynamics model of the developing biofuels industry in the United States. We apply a two-stage sensitivity approach consisting of an initial sensitivity screening, followed by a variance decomposition approach. Identifying key system levers and quantifying their strength is not straightforward in complex system dynamics models that have numerous feedbacks and nonlinear results. Variance-based sensitivity analysis (VBSA) offers a systematic, global approach to assessing system dynamics models because it addresses nonlinear responses and interactive effects. Especially when a large model's size makes manual exploration of the input space difficult and time-consuming, the approach can help to provide a comprehensive understanding of interactions that drive model behaviors.
Keywords
Cite
@article{arxiv.1803.10722,
title = {Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model},
author = {Daniel Inman and Laura J. Vimmerstedt and Brian Bush and Dana Stright and Steve Peterson},
journal= {arXiv preprint arXiv:1803.10722},
year = {2018}
}