Summary function elasticity analysis for an individual-based System Dynamics model

While eigenvalue elasticity analysis can offer insights into System Dynamics model behavior, such analysis is complicated, unwieldy and infeasible for larger models due to superlinear growth of the number of eigenvalue-parameter as the number of stocks rises. To overcome these difficulties, we develop a summary function elasticity analysis method, which aids in analyzing the impact of a parameter on some global summary of the system state. A summary function defines a scalar field over state space summarizing the global state of a system. Summary function elasticity with respect to a parameter measures the ratio of the proportional change in the function to the proportional change in a parameter. We use an individual-based viral spread model to demonstrate that this new method offers greater simplicity than eigenvalue elasticity analysis while retaining most of its advantages. This method can be readily scaled to analyze impacts of parameters on larger-scale System Dynamics models.