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

研讨会：破坏非法供应网络：运筹学和数据分析的新应用以结束现代奴隶制；

• 批准号: 1726895
• 负责人: Noel Busch-Armendariz
• 金额: $ 9.98万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726895&HistoricalAwards=false
• 关键词: Workshop; Disrupting; Illicit; Supply; Networks

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.

该奖项为一个研讨会提供支持，该研讨会将召集一个跨学科的学者团队，为旨在破坏人口贩运等非法供应网络的运筹学和数据分析的应用确定有前途的研究方向。美国国务院认为人口贩运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被欺骗或胁迫。人口贩运是非法网络在社会中运作并危害社会的一个重要例子。其他例子包括武器贩运、毒品贩运、动物贩运和人口走私。通过汇集不同学科的学者，讲习班的目标是确定新的研究方法和突破性战略，以破坏贩运人口中常见的非法网络。该研讨会将包括两次会议，第一次会议将在德克萨斯大学奥斯汀分校举行，第二次会议将在华盛顿特区举行，将使来自运筹学、管理科学、分析学、机器学习和数据科学的学者交流思想，并概述针对非法网络的破坏性干预发展的潜在研究议程。本次讲习班制定的议程将有助于将对此类非法系统的理解从描述性描述和预测性估计转向改进动态操作控制。OR和数据分析是将这一视角引入非法网络研究的理想领域。很少有研究从动态系统理论的角度来解决这个问题，该理论允许将社会正义挑战表示为一个数学系统，可以根据决策变量进行分析，以帮助指导、控制和约束行为动态以实现预期目标。纠正非法网络影响的解决办法本质上是跨学科的，通常涉及刑事司法、社会工作、社会科学、经济学、保健和法律等领域。这种系统是动态的，剥削和伤害社区成员。更重要的是，它们同时涉及合法和非法活动，这往往使犯罪活动与执法混淆。这些系统可以是由相互关联的活动和参与者组成的分层、非平稳网络，涉及犯罪者、受害者和/或旁观者的交叉决策。在这类系统中非常常见的是数据缺乏，这在很大程度上是由于犯罪的隐藏方面和相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；使用OR方法探索可行的现实世界解决方案的形式和复杂性；评估问题建模和分析所需的特征和数据量；提出研究议程，指导跨学科学者团队制定方法和解决方案；在研讨会参与者和他们的研究合作者（包括初级研究者和研究生）之间发起和促进持续的互动。