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Maryland Smith’s Raghavan Heads NSF Research on Illicit Drug Trafficking

Feb 11, 2021
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Maryland Smith’s S. Raghu Raghavan is the principal investigator for a five-year National Science Foundation collaborative research award worth nearly $1 million to look into drug cartels: Discovery, Analysis, and Disruption of Illicit Narcotic Supply Networks.

As transnational drug cartels continue to grow in size and scope, their trafficking networks have become more complex and fragmented. This project takes a multidisciplinary, scientific approach to build better insight and optimization of U.S. counternarcotics efforts. The researchers will refine analytic methods to develop an understanding of the network structure and models of the flow of cocaine. The research will support the disruption strategies of anti-narcotics and other law enforcement agencies.

Joining Raghavan on the $743,806 University of Maryland portion of the project are co-PIs Margret Bjarnadottir, associate professor at Maryland Smith, John Dickerson (CS), Greg Midgette (Criminology and Criminal Justice) and Marcus Boyd (START).

The Maryland team is joined by collaborators Siddharth Chandra (PI) and Galia Benitez (co-PI), who are working on a $256K companion project at Michigan State University.

The research is funded by NSF’s Directorate for Social Behavioral and Economic Sciences (SBE) and is part of the SBE Office of Multidisciplinary Activities’ Disrupting Operations of Illicit Supply Networks program.

Raghavan and his colleagues will analyze the dynamics of narcotic supply networks and how interdiction strategies disrupt them. The team’s approach will encompass operations research, computer science, criminology, public policy, geographic, and economics. Network analysis of temporal and spatial cocaine price data will be used to infer illicit supply chain network structure and flow. Artificial intelligence and learning models will be applied to the empirical data to extract network behavior in response to interdiction activities, while game theoretic models will blend combinatorial optimization and agent-based simulation to evaluate the outcomes of various interdiction strategies.

The research results will be integrated into a network optimization model to explain the structure of illicit drug supply chains and provide evidence to support successful disruption strategies.

Originally posted by the University of Maryland’s Institute for Systems Research.

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