Disrupting Human Trafficking Networks Through Mathematical Modeling: Addressing Replacement Effects and Uncertain Information, 2020 (ICPSR 39827)
Principal Investigator(s): View help for Principal Investigator(s)
Daniel Kosmas, Rensselaer Polytechnic Institute;
John E. Mitchell, Rensselaer Polytechnic Institute
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This project developed novel prescriptive analytics to assist criminal justice practitioners in the decision-making process in disrupting human trafficking networks, as well as develop a network generator that will simulate human trafficking networks. These analytics will be based on network interdiction problems, a class of mathematical problems that have been successfully applied to the disruption of nuclear smuggling and drug trafficking networks. Current limitations on applying network interdiction problems to disrupting human trafficking is the uncertainty in the network structure and that traffickers will react to any disruption efforts to mitigate their losses. We improved upon network interdiction models by incorporating how traffickers might react in response to disruption efforts. These models were tested on synthetic but realistic network data on domestic sex trafficking networks.