Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression
Abstract
An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease states in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of themore »
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Science and Engineering Division
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1319161
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Open Journal of Modelling and Simulation
- Additional Journal Information:
- Journal Volume: 03; Journal Issue: 04; Journal ID: ISSN 2327-4018
- Publisher:
- Scientific Research Publishing
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; agent-based simulation; diabetes progression; healthcare; health behavior modeling
Citation Formats
Schryver, Jack, Nutaro, James, and Shankar, Mallikarjun. Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression. United States: N. p., 2015.
Web. doi:10.4236/ojmsi.2015.34019.
Schryver, Jack, Nutaro, James, & Shankar, Mallikarjun. Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression. United States. https://doi.org/10.4236/ojmsi.2015.34019
Schryver, Jack, Nutaro, James, and Shankar, Mallikarjun. Fri .
"Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression". United States. https://doi.org/10.4236/ojmsi.2015.34019. https://www.osti.gov/servlets/purl/1319161.
@article{osti_1319161,
title = {Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression},
author = {Schryver, Jack and Nutaro, James and Shankar, Mallikarjun},
abstractNote = {An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease states in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.},
doi = {10.4236/ojmsi.2015.34019},
journal = {Open Journal of Modelling and Simulation},
number = 04,
volume = 03,
place = {United States},
year = {Fri Oct 30 00:00:00 EDT 2015},
month = {Fri Oct 30 00:00:00 EDT 2015}
}
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