D-ISN: TRACK 1: Collaborative Research: Discovery, Analysis, and Disruption of Illicit Narcotic Supply Networks

随着跨国毒品卡特尔的规模和范围不断扩大，其贩运网络变得更加复杂和分散。这个破坏非法供应网络（D-ISN）项目采取多学科的科学方法，以更好地了解和优化美国的禁毒工作。它改进了分析方法，以了解可卡因流动的网络结构和模型，从而支持禁毒和其他执法机构的破坏战略。该项目分析了麻醉品供应网络的动态以及阻断战略如何破坏这些网络。采用融合的方法，结合运筹学，计算机科学，犯罪学，公共政策，地理和经济的角度来看，本研究利用网络分析的时间和空间可卡因价格数据来推断非法供应链网络结构和流量。人工智能和学习模型应用于经验数据，以提取网络行为，以响应阻断活动，而博弈论模型融合组合优化和基于代理的模拟，以评估各种阻断策略的结果。研究结果将被整合到一个网络优化模型中，以解释非法药物供应链的结构，并为支持成功的破坏战略提供证据。该奖项反映了NSF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。

As transnational drug cartels continue to grow in size and scope, their trafficking networks have become more complex and fragmented. This Disrupting Operations of Illicit Supply Networks (D-ISN) project takes a multi-disciplinary, scientific approach to build better insight and optimization of counter-narcotics efforts in the United States. It refines analytic methods to develop an understanding of the network structure and models of the flow of cocaine, which supports disruption strategies of anti-narcotics and other law enforcement agencies.The project analyzes the dynamics of narcotic supply networks and how interdiction strategies disrupt these networks. Employing a convergent approach that combines operations research, computer science, criminology, public policy, geographic, and economic perspectives, this research exploits network analysis of temporal and spatial cocaine price data to infer illicit supply chain network structure and flow. Artificial intelligence and learning models are applied on the empirical data to extract network behavior in response to interdiction activities, while game theoretic models blend combinatorial optimization and agent-based simulation to evaluate the outcomes of various interdiction strategies. Results will be integrated into a network optimization model to explain the structure of illicit drug supply chains and provide evidence to support successful disruption strategies.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.