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 tested 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 the model's design. We demonstrate ANTs by testing a well-known model of global dynamics (World 3), and show how this technique can be used to uncover small, but powerful, nonlinear effects that may highlight vulnerabilities in the original model.
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Paper provided by Santa Fe Institute in its series Working Papers with number
96-03-011.
For technical questions regarding this item, or to correct its listing, contact: (Thomas Krichel).
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