  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.
