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Drugtalk

by Michael Agar (Submitted: 09/19/2004)

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Drugtalk models how experiences with an illicit drug, evaluations of those experiences transmitted through social and spatial networks, and encounters with addicted agents lead to different rates of use and addiction. The model is described in detail and discussed in the context of ethnographic research and model validity in a forthcoming article in the Journal of Artificial Societies and Social Simulation entitled "Real Agents and Simulated Agents: "Emics" and "Etics" in Artificial Societies by Michael Agar (magar@anth.umd.edu).

The current model always uses 500 agents and assigns a "risk" value based on a random-normal distribution. An agent's risk value does not change. "Attitude" is set with a slider on the interface and all agents receive that value initially. Agent attitudes change with experience, those changes shown on the "Attitude" bar graph on the interface. A red patch will appear at the center, that patch representing availability of a new drug. The number of patches will increase with time if use takes off. The rapidity of growth of new patches can be adjusted with the "demand-response" parameter on the interface.

An agent is "at-risk" if its risk value is greater than its attitude value. If it is at-risk, and it lands on a red patch, or if an agent in its network offers the drug to it, it will use. The "users/dependent" graph shows the number of agents who are at-risk, who have used at least once, and who are addicted. "Addicts" are agents who have used a certain number of times, that number set as a parameter on the "uses-to-habit" slider on the interface.

"Badstuff?" and "Goodstuff?" sliders on the interface vary the average quality of the drug experience. The higher the number, the more likely the experience will be positive or negative. Note that the model allows both results to occur--an experience can be both good and bad.

Comments and suggestions welcome.

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