EAGER: ISN: Disrupting Exploitation and Trafficking Labor Supply Networks in Post-Harvey Rebuild

这个早期概念的探索性研究赠款项目（EAGER）将通过提高对自然灾害后混乱的重建和恢复环境中经常发生的劳动力剥削和贩运状况的理解，为国家安全，繁荣和福利做出贡献。建筑工人极易受到剥削和人口贩运，特别是在灾后环境中。通过对休斯顿地区劳工的回顾性访谈，该项目将促进对哈维飓风后重建工作中建筑业非法供应网络的科学理解，并评估其破坏的潜在对策。该项目将是打击其他行业人口贩运的第一步。它将涉及早期的职业学者，研究生，妇女，少数民族和多个机构，并将更广泛地促进未来的业务研究界参与破坏非法供应网络。该研究项目将解决复杂的灾后劳动力贩运生态系统中遇到的几个新颖而独特的特征，包括具有部分信息的随机系统，以捕获无法直接观察到劳动者状态的情况。此外，建筑工人的就业状况一般变化比较频繁。 这些状态或状态变化以及生态系统参与者的相关决策和行动将需要研究人员探索多期动态随机博弈在捕捉这些动态方面的优点。 计划中的多代理随机决策框架将基于来自回顾性实地采访的真实证据开发，其中劳动者将提供自飓风哈维或他们抵达受影响地区以来所经历的就业和行为的回顾轨迹。 受害者的部分可观察性和贩运者的适应行为也将被捕获。 随机模型的开发将得到多智能体仿真模型的支持和评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 EArly-concept Grants for Exploratory Research (EAGER) project will contribute to the national security, prosperity and welfare by improving understanding of the states of laborers exploitation and trafficking that often occurs in the chaotic rebuild and recovery environment following a natural disaster. Construction workers are extremely vulnerable to exploitation and human trafficking, especially in the post-disaster settings. Through retrospective interviews among Houston-area laborers, this project will facilitate scientific understanding of the illicit supply networks in the construction industry in the post-Harvey rebuilding efforts and evaluate potential countermeasures for their disruption. The project will be a first step toward disrupting human trafficking in other industry sectors. It will involve early-career scholars, graduate students, women, minorities, and multiple institutions, and will more broadly facilitate future involvement of the operations research community in the disruption of illicit supply networks. This research project will address several novel and unique features encountered in the complex post-disaster labor trafficking ecosystem, including stochastic systems with partial information to capture the case where a laborer's state cannot be directly observed. In addition, the employment status of construction laborers generally changes relatively frequently. These status or state changes and the associated decisions and actions by ecosystem participants will require researchers to explore the merit of multi-period dynamic stochastic games in capturing these dynamics. The planned multi-agent stochastic decision-making framework will be developed based on real-world evidence from the retrospective field interviews wherein laborers will provide a recalled trajectory of employment and behaviors experienced since Hurricane Harvey or their arrival to the affected region. The partial observability of victims and adaptive behavior of the traffickers will also be captured. Stochastic model development will be supported and evaluated with multiagent simulation models.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.