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Automated parameter specification in dynamic feedback models based on behavior pattern features

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  • Gönenç Yücel
  • Yaman Barlas

Abstract

In various stages of a modeling study, the developer/user encounters the need for identification of specific values for certain model parameters. However, due to the non‐linear nature of these models it is generally hard to foresee changes in the dynamic behavior as a consequence of even marginal parameter changes. Thus, the need for an automated and efficient search approach is very clear. A behavior pattern‐based parameter search approach and results obtained from various test experiments are presented in this paper. The pattern identification algorithm used enables a parameter search process based on qualitative features of a desired behavior pattern, even in the absence of a reference data series. The approach conducts the search process so as to obtain values for the model parameters that yield an output that matches the desired behavior in terms of its qualitative pattern features. Experiments conducted reveal the viability of the approach as a support tool that can be used in model calibration, sensitivity analysis, validation, and policy design stages. Copyright © 2011 System Dynamics Society.

Suggested Citation

  • Gönenç Yücel & Yaman Barlas, 2011. "Automated parameter specification in dynamic feedback models based on behavior pattern features," System Dynamics Review, System Dynamics Society, vol. 27(2), pages 195-215, April.
    • Handle: RePEc:bla:sysdyn:v:27:y:2011:i:2:p:195-215
    DOI: 10.1002/sdr.457
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    1. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Other publications TiSEM 87ee6ee0-592c-4204-ac50-6, Tilburg University, School of Economics and Management.
    2. John H. Miller, 1998. "Active Nonlinear Tests (ANTs) of Complex Simulation Models," Management Science, INFORMS, vol. 44(6), pages 820-830, June.
    3. R G Coyle, 1999. "Simulation by repeated optimisation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(4), pages 429-438, April.
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    1. Mert Edali & Gönenç Yücel, 2020. "Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(6), pages 936-958, November.
    2. Mert Edali, 2022. "Pattern‐oriented analysis of system dynamics models via random forests," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 135-166, April.

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