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

这一破坏非法供应网络运作（D-ISN）项目将通过提高对在飓风和流行病等自然灾害后混乱的重建和恢复环境中经常发生的劳工剥削和贩运状况的了解，为国家安全、繁荣和福利做出贡献。建筑供应链极易受到劳动力剥削和贩运的影响，尤其是对技能较低的工人和临时工而言。基于模型的决策框架将通过设计更有效的干预措施，帮助政策制定者和个人从社会角度预防和应对劳动剥削和贩运的情况。该项目将为设计和部署大规模干预目标和计划试验提供一条道路。COVID-19等持续的自然灾害对劳动力供应链的影响日益加剧，这使得决策框架的制定具有内在的弹性。该项目将涉及早期职业学者、研究生、妇女、少数民族和多个机构，并将更广泛地促进行为科学和决策科学社区未来参与破坏非法供应网络。该研究项目将解决复杂劳动力供给生态系统中遇到的几个新颖和独特的特征，包括具有部分信息的随机系统，以捕捉无法直接观察到劳动者状态和与其他人互动的情况。此外，建筑工人的就业状况一般变化比较频繁。拟议的框架将为政策制定者、监管机构和公司提供管理见解，指导他们如何在资源有限的情况下监控、打击和破坏劳动力剥削和贩运。本研究有几个新颖而独特的方面，因为它努力捕捉相关复杂生态系统中遇到的以下关键特征：实证研究、随机多参与者网络模型、基于智能体的仿真模型和批量强化学习。该项目将行动研究和社区业务研究结合起来，创建、测试和完善一个框架，用于设计和评估以纠正人类非法行为为重点的干预措施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.