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

Authored by Daniel Heard, Georgiy V. Bobashev, Robert J. Morris

Date Published: 2014-07-15

DOI: 10.1371/journal.pone.0102263

Sponsors: United States National Institutes of Health (NIH)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

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.

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