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Active Nonlinear Tests (ANTs) of Complex Simulation Models

Author

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  • John H. Miller

    (Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

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.

Suggested Citation

  • John H. Miller, 1998. "Active Nonlinear Tests (ANTs) of Complex Simulation Models," Management Science, INFORMS, vol. 44(6), pages 820-830, June.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:6:p:820-830
    DOI: 10.1287/mnsc.44.6.820
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    References listed on IDEAS

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    1. Steve Bankes, 1993. "Exploratory Modeling for Policy Analysis," Operations Research, INFORMS, vol. 41(3), pages 435-449, June.
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