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                            Funding & Awards
 Disrupting Human Trafficking Networks through Mathematical Modeling: Addressing Replacement Effects and Uncertain Information
Award Information
Awardee
              RENSSELAER POLYTECHNIC INSTITUTE
Award #
              2020-R2-CX-0022
Funding Category
              Competitive Discretionary
Location
                    TROY, NY
Congressional District
              20
Status
              Past Project Period End Date
Funding First Awarded
              2020
Total funding (to date)
              $100,000
Original Solicitation
              Graduate Research Fellowship, Fiscal Year 2020
Description of original award (Fiscal Year 2020, $100,000)
Human trafficking affects millions of people, a majority of whom have yet to be identified, and generates millions in illicit profits. Despite a growing awareness of the magnitude of the problem, there is a lack of quantitative methodology to assist policymaking in combating human trafficking. Two of the challenges of applying quantitative methodology to disrupting human trafficking networks are a lack of data on human trafficking networks and limited work in modeling how human trafficking networks operate. The purpose of this project is to develop 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. The generator will be developed from quantitative network data based on qualitative data collected by a team of social science researchers at the University of Minnesota. The team has interviewed trafficking survivors and those that are involved in organizations supporting survivors to understand the operations of different types of trafficking operations. In addition, the team has reviewed law enforcement case files (both those available in the public record and investigator notes) on several trafficking operations in the state of Minnesota. 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 plan to expand upon decomposition algorithms, the current solution methodology to these problems, in three stages. We will first work to solve network interdiction problems with incomplete information and network restructuring. We then will work to solve multi-period network interdiction problems with network restructuring. The project will conclude with solving multi-period network interdiction problems with incomplete information and network restructuring. These analytics will be validated, with the assistance of the team of social scientists at the University of Minnesota, on networks produced by the network generator. The network generator and analytics will be made publicly available. With the assistance of these tools, criminal justice practitioners will be able to better allocate resources in the efforts to combat human trafficking, reducing costs associated with combatting human trafficking and allowing them to assist more survivors of human trafficking.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR  200.210(a)(14).  CA/NCF



  
Grant-Funded Publications
        
                      Interdicting Restructuring Networks with Applications in Illicit Trafficking
                  A transdisciplinary approach for generating synthetic but realistic domestic sex trafficking networks
                  Multi-period max flow network interdiction with restructuring for disrupting domestic sex trafficking networks

  
Date Created: September 20, 2020
    
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