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

## TL;DR

This paper demonstrates how variance-based sensitivity analysis can be effectively applied to large, complex system dynamics models like the Biomass Scenario Model to identify key drivers and understand interactions.

## Contribution

It introduces a novel two-stage sensitivity approach combining screening and variance decomposition for complex system dynamics models.

## Key findings

- Identified key system levers in the biomass industry model.
- Quantified the strength of different system interactions.
- Showed the effectiveness of VBSA in complex nonlinear models.

## 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.