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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
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    File URL: http://dx.doi.org/10.1287/mnsc.44.6.820
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    References listed on IDEAS

    as
    1. Nordhaus, William D, 1973. "World Dynamics: Measurement Without Data," Economic Journal, Royal Economic Society, vol. 83(332), pages 1156-1183, December.
    2. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
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    Cited by:

    1. Haruvy, Ernan & Roth, Alvin E. & Unver, M. Utku, 2006. "The dynamics of law clerk matching: An experimental and computational investigation of proposals for reform of the market," Journal of Economic Dynamics and Control, Elsevier, vol. 30(3), pages 457-486, March.
    2. Sibel Eker & Jill Slinger & Els Daalen & Gönenç Yücel, 2014. "Sensitivity analysis of graphical functions," System Dynamics Review, System Dynamics Society, vol. 30(3), pages 186-205, July.
    3. William Tracy, 2014. "Paradox Lost: The Evolution of Strategies in Selten’s Chain Store Game," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 83-103, January.
    4. Jan H. Kwakkel & Erik Pruyt, 2015. "Using System Dynamics for Grand Challenges: The ESDMA Approach," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(3), pages 358-375, May.
    5. 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.
    6. Chaturvedi, Alok & Mehta, Shailendra & Dolk, Daniel & Ayer, Rick, 2005. "Agent-based simulation for computational experimentation: Developing an artificial labor market," European Journal of Operational Research, Elsevier, vol. 166(3), pages 694-716, November.
    7. 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.
    8. 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.
    9. Kwakkel, Jan H. & Pruyt, Erik, 2013. "Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 419-431.
    10. Eva Labro, 2015. "Using simulation methods in accounting research," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 26(2), pages 99-104, August.

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