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Researching a local heroin market as a complex adaptive system
Lee D Hoffer1, Georgiy Bobashev, Robert J Morris
1Department of Anthropology, Case Western Reserve University, 11220 Bellflower Road #205, Cleveland, OH 44106, USA. lee.hoffer@case.edu
American Journal of Community Psychology
|October 20, 2009
Summary
Agent-based modeling (ABM) simulated a Denver heroin market using ethnographic data. This novel approach offers insights into illicit drug market dynamics and outcomes.
Area of Science:
- Computational social science
- Epidemiology
- Criminology
Background:
- Understanding illicit drug markets is crucial for public health and safety interventions.
- Previous research often lacks detailed insights into the operational dynamics of local drug markets.
- Ethnographic data provides rich, contextual information on user and dealer behaviors.
Purpose of the Study:
- To apply agent-based modeling (ABM) to simulate and analyze a local heroin market.
- To understand the operation, organization, and structure of the Larimer area heroin market in Denver.
- To explore the aggregated outcomes of individual agent interactions within the simulated market.
Main Methods:
- Utilized an 18-month ethnographic case study conducted in Denver during the 1990s.
- Developed an agent-based model incorporating data on customers, private dealers, street-sellers, brokers, and police.
- Evaluated logical consistency between ethnographic data and agent behaviors, then scaled up simulations.
Main Results:
- Simulations revealed aggregated outcomes resulting from scaled-up interactions between market agents.
- The study provides a novel computational approach to understanding illicit drug market dynamics.
- Findings offer insights into the complex interplay of behaviors within a historical heroin market.
Conclusions:
- Agent-based modeling offers a powerful, novel methodology for studying illicit drug markets.
- The simulation provides a framework for understanding market structure, organization, and emergent outcomes.
- This research highlights the potential for computational methods to inform public health and policy regarding drug markets.
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