Connecting micro dynamics and population distributions in system dynamics models

Authors

  • Saeideh Fallah-Fini,

    Corresponding author
    1. Industrial and Manufacturing Engineering Department, California State Polytechnic University, Pomona, CA, U.S.A.
    • Correspondence to: Saeideh Fallah-Fini, Industrial and Manufacturing Engineering Department, California State Polytechnic University, Pomona, CA 91768, U.S.A. E-mail: sfallahfini@csupomona.edu

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  • Hazhir Rahmandad,

    1. Industrial and Systems Engineering Department, Virginia Tech, Falls Church, VA, U.S.A.
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  • Hsin-Jen Chen,

    1. Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.
    2. Institute of Public Health, National Yang-Ming University, Taiwan, Republic of China
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  • Hong Xue,

    1. Johns Hopkins Global Center On Childhood Obesity, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, U.S.A.
    2. Department of Epidemiology and Environmental Health (formerly Department of Social and Preventive Medicine), School of Public Health and Health Professions, University at Buffalo, State University of New York, Buffalo, NY, U.S.A.
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  • Youfa Wang

    1. Johns Hopkins Global Center On Childhood Obesity, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, U.S.A.
    2. Department of Epidemiology and Environmental Health (formerly Department of Social and Preventive Medicine), School of Public Health and Health Professions, University at Buffalo, State University of New York, Buffalo, NY, U.S.A.
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Abstract

Researchers use system dynamics models to capture the mean behavior of groups of indistinguishable population elements (e.g. people) aggregated in stock variables. Yet many modeling problems require capturing the heterogeneity across elements with respect to some attribute(s) (e.g. body weight). This paper presents a new method to connect the micro-level dynamics associated with elements in a population with the macro-level population distribution along an attribute of interest without the need to explicitly model every element. We apply the proposed method to model the distribution of body mass index and its changes over time in a sample population of American women obtained from the U.S. National Health and Nutrition Examination Survey. Comparing the results with those obtained from an individual-based model that captures the same phenomena shows that our proposed method delivers accurate results with less computation than the individual-based model. Copyright © 2014 System Dynamics Society

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