D-ISN: TRACK 1: Collaborative Research: Disrupting Exploitation and Trafficking in Labor Supply Networks: Convergence of Behavioral and Decision Science to Design Interventions

This Disrupting Operations of Illicit Supply Networks (D-ISN) project will contribute to the national security, prosperity, and welfare by improving understanding of the states of laborer exploitation and trafficking that often occurs in the chaotic rebuild and recovery environment following natural disasters such as hurricanes and pandemics. Construction supply chains are extremely vulnerable to labor exploitation and trafficking, especially among workers with fewer skills and day laborers. The model-based decision framework will help policy makers and individuals prevent and respond to situations of labor exploitation and trafficking from a societal perspective by designing more efficacious interventions. The project will provide a path forward toward the design and deployment of a large-scale trial of interventional targets and programs. The exacerbating influences that ongoing natural disasters like COVID-19 have on labor supply chains allow the decision framework to be developed with built-in resilience. The project will involve early-career scholars, graduate students, women, minorities, and multiple institutions, and will more broadly facilitate future involvement of the behavioral science and decision science communities in the disruption of illicit supply networks.

This research project will address several novel and unique features encountered in the complex labor supply ecosystem, including stochastic systems with partial information to capture the case where a laborer's state and interactions with others cannot be directly observed. In addition, the employment status of construction laborers generally changes relatively frequently. The proposed framework will provide managerial insights to policy makers, regulators, and companies on how to monitor, combat, and disrupt labor exploitation and trafficking with limited resources. This research has several novel and unique aspects as it strives to capture the following critical features encountered in the relevant complex ecosystem: empirical research, stochastic multi-actor network models, agent-based simulation models, and batch reinforcement learning. The project combines action research and community operations research to create, test, and refine a framework for designing and evaluating interventions whose focus is to remediate illicit human behavior.

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.