Workshop: Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery; Austin, Texas, and Washington, DC

该奖项为一个研讨会提供支持，该研讨会召集了一个跨学科的学者团队，以确定有前途的研究方向，用于旨在破坏非法供应网络（如人口贩运）的运筹学（OR）和数据分析的应用。 美国国务院认为人口贩卖是现代奴隶制的一种形式，并将其广义地定义为一个人在卖淫、强迫劳动或家庭奴役的情况下受到欺骗或胁迫。 人口贩运是在社会中运作并危害社会的非法网络的一个重要例子。 其他例子包括武器贩运、毒品贩运、动物贩运和人口走私。研讨会的目标是通过汇集来自不同学科的学者，确定新的研究方法和突破性战略，以破坏人口贩运中常见的非法网络。研讨会将包括两次会议，第一次将在德克萨斯大学奥斯汀分校举行，第二次将在华盛顿特区举行，将使运筹学、管理学、分析，机器学习和数据科学，以交流思想，并概述潜在的研究议程，以制定针对非法网络的破坏性干预措施。 本次讲习班制定的议程将有助于推动对这类非法系统的理解，从描述性特征和预测性估计转向改进动态业务控制。 OR和数据分析是非常适合将这种观点引入非法网络研究的领域。 很少有研究从动态系统理论的角度来探讨这个问题，这种理论允许社会正义挑战被表示为一个数学系统，可以根据决策变量进行分析，以帮助引导、控制和约束行为动态，从而实现预期的目标。 补救非法网络影响的解决方案本质上是跨学科的，通常涉及刑事司法，社会工作，社会科学，经济学，医疗保健和法律领域。 这种系统是动态的，剥削和伤害社区成员。更有甚者，它们同时涉及法律的和非法的活动，这往往使犯罪活动无法得到执法。 这些系统可以是分层的，相互关联的活动和参与者的非静态网络，涉及犯罪者，受害者和/或旁观者的交叉决策。 这类系统中非常常见的是数据不足，这在很大程度上是由于犯罪的隐蔽方面和有关人口的部分可观察性。 讲习班的目的包括在分析和建模框架内审查非法网络的结构和性质;利用OR方法探索可行的现实世界解决方案的形式和复杂性;评估建模和分析问题所需的数据的特点和数量;提出一个研究议程，以指导跨学科学者团队制定方法和解决方案的努力;发起和促进研讨会与会者及其研究合作者（包括初级研究人员和研究生）之间的持续互动。

This award provides support for a workshop to convene an interdisciplinary team of scholars to identify promising research directions for applications of operations research (OR) and data analytics aimed at the disruption of illicit supply networks such as human trafficking. The United States Department of State considers human trafficking a form of modern-day slavery and broadly defines it to be when a person is deceived or coerced in situations of prostitution, forced labor, or domestic servitude. Human trafficking is a key example of illicit networks that operate in and to the detriment of society. Other examples include arms trafficking, drug trafficking, animal trafficking, and human smuggling. By bringing together scholars from disparate disciplines, the goal of the workshop is to identify new research approaches and breakthrough strategies for disrupting the illicit networks commonly found in human trafficking.The workshop, which will consist of two meetings, the first to be held at The University of Texas at Austin and the second to be held in Washington, DC, will enable scholars from operations research, management science, analytics, machine learning, and data science to exchange ideas and outline a potential research agenda for the development of disruptive interventions against illicit networks. The agenda developed at this workshop will help move understanding of such illicit systems from descriptive characterization and predictive estimation toward improved dynamic operational control. OR and data analytics are fields ideally suited to bring this perspective to the study of illicit networks. Few studies have approached this problem from a dynamic systems theoretical perspective that allows the social justice challenge to be represented as a mathematical system that can be analyzed in terms of decision variables to help guide, control, and constrain behavioral dynamics toward desired goals. Solutions to remediate the effect of illicit networks are inherently interdisciplinary, typically involving the fields of criminal justice, social work, social science, economics, healthcare, and law. Such systems are dynamic and exploit and victimize members of the community. What is more, they involve both legal and illicit activities at the same time, which often obscures criminal activity from law enforcement. These systems can be hierarchical, nonstationary networks of interconnected activities and participants that involve intersectional decision making by perpetrators, victims, and/or bystanders. Highly common among such systems is a paucity of data due in large part to the hidden aspects of the crime and the partial observability of the population of interest. The workshop aims include examining the structure and nature of illicit networks within an analytic and modeling framework; exploring the form and complexity of viable, real-world solutions using OR methodologies; assessing the characteristics and amount of data needed to model and analyze the problem; proposing a research agenda to guide the efforts of interdisciplinary teams of scholars to develop methods and solutions; and initiating and facilitating ongoing interactions among workshop attendees and their research collaborators, including junior investigators and graduate students.