Modeling the impact of government controlled factors on the illegal drug trafficking supply chain

Abstract

Following the construction and validation of the model, a full factorial experiment was run testing four major government affect variables. These were police budget, police focus (on enforcement or information-gathering), rehabilitation budget, and finally rehabilitation success. Each of the factors had 3 levels, resulting in 81 different experiments varying government resource allocation. Descriptive and inferential analysis was conducted on the results. The analysis offers insights and possible policy changes that could provide value to the war on drugs. These insights include: (1) there is a significant interaction between the rehabilitation budget and the police budget which needs to be considered to attack the demand side of the market, (2) with a non-finite population the relapse rate has a rather low impact on the response variables measured, and, therefore, the return on investment into rehabilitation is similar for a variety of relapse rates, finally (3) focusing resources on gathering information to arrest high-ranking criminals and disrupt higher levels of the supply chain has a smaller impact on the response factors than focusing on street-level enforcement.

In this thesis, a simulation is constructed to model the illegal drug supply chain interactions with user networks, law enforcement networks, and rehabilitation networks at the city level with the aim to gain insight as to the effects of state-level government resource allocation. The majority of the policy surrounding the drug markets has been aiming at reducing the supply side of the illegal drug market, where our model is capable of analyzing both policies aimed at reducing the supply side and policies aimed at reducing the demand side. The model built incorporates the street level interactions between the aforementioned entities and tests the effects of changes in government controlled resources.