Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model

Daniel Inman, Laura Vimmerstedt, Brian Bush, Dana Stright

Research output: Contribution to journalArticle

14 Scopus Citations


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.
Original languageAmerican English
Number of pages23
StatePublished - 2018

NREL Publication Number

  • NREL/JA-6A20-70809


  • biofuel
  • biomass
  • R statistical programming language
  • statistical programming
  • system dynamics
  • variance based sensitivity analysis


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