This award will contribute to the advancement of national health, prosperity and welfare by studying the efficient allocation of resources over time to disrupt human trafficking networks. A key, and largely understudied, means to disrupt human trafficking is by reducing the supply of potential victims through a better understanding of the critical role that housing plays in individual vulnerability. In order to reduce the population at risk of being trafficked (or re-trafficked), this project provides a need-based prevalence estimation of homeless youth in New York City and an optimization-based approach to determine the most efficient use of scarce shelter and service resources. Shelter and associated rehabilitative services disrupt human trafficking networks by decreasing future vulnerability and recidivism for those at-risk of trafficking. This award will help guide a long-term, cost-effective intervention approach by developing an optimal policy for deployment of marginal temporal housing and services capacity. The interdisciplinary and collaborative nature of the project team ensures that the results of this research will have a practical impact and be disseminated widely within the broader anti-human trafficking research community. The project provides an opportunity for a diverse team of doctoral students in operations research, data science, and criminology to work together on a pressing societal problem.
This award will support a mixed-methods research approach that involves both data collection and analytical model formulation to analyze capacity needed to reduce the supply of potential human trafficking victims. The project will employ an innovative capture-recapture sampling strategy to understand the importance of shelter in a population of unstably housed youth at high risk for trafficking. Together with data from their service providers, the project will establish a baseline assessment of the collective needs of vulnerable populations. With this needs assessment, a multiple multidimensional knapsack problem is formulated that allows for time-dependent capacity expansion and allocation and incorporates stochastic arrivals and length of stay. The project determines the marginal deployment of limited shelter capacity to optimize the societal benefit-to-cost ratio.
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.
