Title Active Nonlinear Tests (ANTs) of Complex Simulation Models
 Collaborators John Miller (CMU)
 Keywords Testing Complex Simulation Models, Model Validation,
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.
 Related Links http://zia.hss.cmu.edu/miller/