A sequential model for cracking down on street markets for illicit drugs

This paper develops a sequential decision-making model for assisting law enforcement officials in allocating resources during a crackdown operation on illicit drug markets. The sequential crackdown model (SCM) considers a probabilistic framework, where the probability of incarceration of a dealer and the probability of dealing are modeled as a function of the size of a drug market, crackdown enforcement level, drug dealer's financial hardship, and other market characteristics. The model was developed and tested in consultation with enforcement officials from Philadelphia, PA and Camden, NJ. We present a detailed, step-by-step implementation scheme for updating parameters on each day of the crackdown. Parameter estimation along with examples of model usage is provided. Through these examples, we illustrate how the SCM could be helpful in understanding the response of illicit drug markets to various enforcement strategies. We further show conditions under which an alternating crackdown policy (referred to as a crackdown-backoff) or a consistent use of maximum possible enforcement would be optimal strategies for managing a drug crackdown operation. Within the context of the model and parameter estimates, we show that a much quicker and less costly collapse could be implemented if the available enforcement resources are increased. Finally, the model provides possible conditions under which a crackdown operation would be unsuccessful in eliminating a drug market.