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Active Nonlinear Tests (ANTs) of Complex Simulation Models

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Author Info

  • John H. Miller

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

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    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.44.6.820
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 44 (1998)
    Issue (Month): 6 (June)
    Pages: 820-830

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    Handle: RePEc:inm:ormnsc:v:44:y:1998:i:6:p:820-830

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    Related research

    Keywords: Testing Simulation Models; Nonlinear Sensitivity Analysis; Validation; World3 Model; Genetic Algorithms;

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    Cited by:
    1. Ernan Haruvy & Alvin E. Roth & M. Utku Unver, 2004. "The Dynamics of Law Clerk Matching: An Experimental and Computational Investigation of Proposals for Reform of the Market," Experimental 0404001, EconWPA.
    2. Ma, Tieju & Nakamori, Yoshiteru, 2005. "Agent-based modeling on technological innovation as an evolutionary process," European Journal of Operational Research, Elsevier, vol. 166(3), pages 741-755, November.
    3. Oliva, Rogelio, 2003. "Model calibration as a testing strategy for system dynamics models," European Journal of Operational Research, Elsevier, vol. 151(3), pages 552-568, December.
    4. Robalino, David A. & Voetberg, Albertus & Picazo, Oscar, 2002. "The macroeconomic impacts of AIDS in Kenya estimating optimal reduction targets for the HIV/AIDS incidence rate," Journal of Policy Modeling, Elsevier, vol. 24(2), pages 195-218, May.
    5. William Tracy, 2014. "Paradox Lost: The Evolution of Strategies in Selten’s Chain Store Game," Computational Economics, Society for Computational Economics, vol. 43(1), pages 83-103, January.

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