D-ISN: Improving our Understanding of Illegal Opioid Supply Networks

Rand Corporation, Santa Monica CA

Investigators

Abstract

The objective of this Disrupting Operations of Illicit Supply Networks (D-ISN) project is to better understand illicit supply networks by studying the emergence of dangerous illegal synthetic opioids, such as fentanyl, and the rise of counterfeit prescription opioid medications. The project will advance the national health and welfare by addressing a nationwide epidemic of fatal drug overdoses. Through complementary theory development and systematic comparison of illegal opioid markets with other illegal markets (e.g., human trafficking, counterfeit products, wildlife smuggling, etc.), the project will identify and test explanations for key questions prompted by the recent emergence of potent synthetic opioids. An improved understanding of supplier operations, trafficker decision-making, and economic foundations of illegal supply networks will help guide effective policy responses aimed at disrupting supply. For opioids, systematic characterization of supplier decision-making can offer valuable lessons to reduce harms. Contrasting findings for opioids with those for other illegal supply networks may offer additional insights across a range of trafficking domains. This project will draw on methods from economics, operations research, computer science, and social sciences to address three major aims: (1) collect and analyze new data related to the illegal supply of synthetic opioids through web-scraping of online listings for these drugs, reviewing federal court documents for trafficking in synthetic opioids, and conducting interviews with law enforcement involved in countering synthetic opioid trafficking; (2) expand existing theoretical frameworks of illegal markets derived from risks and prices to best explain the patterns associated with the illegal distribution of fentanyl; and (3) synthesize findings across networks for different illicit commodities through the drafting of comparative volumes with external experts in trade in other contraband. Data collection will leverage automated software tools, natural language processing, and semi-structured interviews. Economic and supply chain modeling will be used to interpret data and formulate findings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.