Improving model understanding using statistical screening

System dynamics models are often constructed to improve system performance by identifying and modifying feedback mechanisms that drive system behavior. Once identified, these feedback mechanisms can be used to design and test policies for system performance improvement. A preliminary step in developing policies is the identification of high-leverage parameters and structures, the influential model sections that drive system behavior. The current work clarifies and extends the use of statistical screening as a tool to improve model understanding, explanation, and development with a six-step process. Statistical screening adds rigor to model analysis by objectively identifying high-leverage model parameters and structures for further analysis. Statistical screening offers system dynamicists a user-friendly tool that can be used to help explain how model structure drives behavior.