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Reducing the Complexity of an Agent-Based Local Heroin Market Model

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  • Daniel Heard
  • Georgiy V Bobashev
  • Robert J Morris

Abstract

This project explores techniques for reducing the complexity of an agent-based model (ABM). The analysis involved a model developed from the ethnographic research of Dr. Lee Hoffer in the Larimer area heroin market, which involved drug users, drug sellers, homeless individuals and police. The authors used statistical techniques to create a reduced version of the original model which maintained simulation fidelity while reducing computational complexity. This involved identifying key summary quantities of individual customer behavior as well as overall market activity and replacing some agents with probability distributions and regressions. The model was then extended to allow external market interventions in the form of police busts. Extensions of this research perspective, as well as its strengths and limitations, are discussed.

Suggested Citation

  • Daniel Heard & Georgiy V Bobashev & Robert J Morris, 2014. "Reducing the Complexity of an Agent-Based Local Heroin Market Model," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0102263
    DOI: 10.1371/journal.pone.0102263
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    References listed on IDEAS

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    1. Sterman, J.D., 2006. "Learning from evidence in a complex world," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 505-514.
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    Cited by:

    1. Hang Xiong & Puqing Wang & Georgiy Bobashev, 2018. "Multiple peer effects in the diffusion of innovations on social networks: a simulation study," Journal of Innovation and Entrepreneurship, Springer, vol. 7(1), pages 1-18, December.

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