SimDrug: Exploring the Complexity of Illicit Drug Markets.

pascal perez


Worldwide, illicit drug use and markets can be described as ill-defined, complex, and adaptive systems. Authorities relentlessly try to reduce harm inflicted to individuals and society. But, so far, they struggle to find the right balance between three traditional modes of intervention: law enforcement, treatment, or prevention. In Australia, the period called "the heroin drought" (Dec200-Feb2001) provides a striking example of complex interactions between open activities and hidden forces.
The features of the agent-based model, called SimDrug, include a spatial environment and social agents. The environment is an archetypal representation of an urban center. Each elementary spatial unit corresponds to a street block. Five suburbs were created with different sizes and shapes. SimDrug includes different types of social agents: users, dealers, wholesalers, police constables, and outreach workers. Each type represents a minimum set of characteristics and dynamics that allows the whole artificial population to display most of the properties observed in real societies.
We have taken 1998-2002 as the reference period. In terms of validation, this time bracket gives us the opportunity to test the robustness of the model by comparing a series of micro (agent level) and macro (system level) indicators with corresponding observed data. The model must be able to consistently reproduce pre-drought, crisis, and post-drought dynamics of the system.
The transdisciplinary work plays a paramount role in defining a consensual set of simplified rules for the corresponding agent to ‘behave’ realistically. SimDrug has demonstrated the plausibility of using a multi-agent system model to describe the relationships between law enforcement, treatment, and prevention programs. The model is robust and later versions should assist Policy makers to determine potential scenarios as a result of their intervention.


complex adaptive system, illicit drug use, agent based modeling, law enforcement

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