|Title||Active Nonlinear Tests (ANTs) of Complex Simulation Models|
|Collaborators||John Miller (CMU)|
|Keywords||Testing Complex Simulation Models,
Nonlinear Sensitivity Analysis, World3 Model, Genetic Algorithms
|Abstract||Simulation models are becoming increasingly
common in the analysis of critical scientific, policy, and management
issues. Such models provide a way to analyze complex systems
characterized by both large parameter spaces and nonlinear interactions.
Unfortunately, these same characteristics make understanding
such models using
traditional testing techniques extremely difficult. Here we show how a model's structure and robustness can be validated via a simple, automatic, nonlinear search algorithm designed to actively ``break'' the model's implications. Using the active nonlinear tests (ANTs) developed here, one can easily probe for key weaknesses in a simulation's structure, and thereby begin to improve and refine its design. We demonstrate ANTs by testing a well-known model of global dynamics (World3), and show how this technique can be used to uncover small, but powerful, nonlinear effects that may highlight vulnerabilities in the original model.