I-Corps: Development of machine learning technology for matching under a variety of realistic and largescale preference structures

I-Corps：开发用于在各种现实和大规模偏好结构下进行匹配的机器学习技术

• 批准号: 2133869
• 负责人: Lexin Li
• 金额: $ 5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133869&HistoricalAwards=false
• 关键词: Corps; Development; machine; learning; technology

The broader impact/commercial potential of this I-Corps project focuses on the development of new technologies for achieving efficient two-sided matching under the constraints of fairness and trustworthiness. Numerous technologies presently exist for two-sided matching of supplies and demands. Those assessments that are fair and trustworthy tend to lack efficiency for commercial applications and lack adequate performance specifications, while those with ideal efficiency are too computationally expensive for large-scale markets. The proposed program explores implementation and commercialization opportunities within the project's initial application focus of small to medium-sized businesses in manufacturing. The proposed technologies have a broad application potential, and can materially reduce the time of finding suitable suppliers and lowering prices. Additionally, the longer-term development of the technology may prove disruptive in markets such as call centers and student tutoring websites. The developed technologies may also improve the supply chain and better allocate scarce resources, e.g., vaccines and medicines, while ensuring social efficiency and fairness in the matching process.This I-Corps project is based on the premise that the intersection of state-of-the-art machine learning and economic principles leads to disruptive innovation. The project's technologies offer a fundamentally different approach to two-sided matching than those developed in the past six decades. This project pursues a data-driven solution to a dynamic two-sided matching problem. Previous progress on this project has focused on the theoretical development of the mechanism design and experimental verification of algorithms in small-scale markets with hundreds of users. By contrast, this project enables a widely scalable approach for millions of users to be matched in real-time and allowing realistic uncertain preferences. The approach not only yields maximum efficiency but also guarantees outcomes such as trustworthiness and fairness.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.

该I-Corps项目的更广泛的影响/商业潜力着重于在公平和可信度的限制下发展新技术，以实现有效的双向匹配。目前存在许多用于供应和需求的两侧匹配的技术。那些公平且值得信赖的评估往往缺乏商业应用的效率，并且缺乏适当的性能规格，而那些理想效率的评估对于大型市场而言，计算机上的计算太昂贵。拟议的计划探讨了项目在制造业中中小型企业的最初应用重点内的实施和商业化机会。拟议的技术具有广泛的应用潜力，可以实质性地减少寻找合适的供应商并降低价格的时间。此外，该技术的长期开发可能会在呼叫中心和学生辅导网站等市场中证明具有破坏性。开发的技术还可以改善供应链，并更好地分配稀缺资源，例如疫苗和药物，同时确保在匹配过程中的社会效率和公平性。该I-Corps项目基于以下前提：最先进的机器学习和经济原理的相交导致了颠覆性的创新。该项目的技术提供了两侧匹配的根本不同的方法，与过去六十年中开发的技术相比。该项目为动态的双面匹配问题寻求数据驱动的解决方案。该项目的先前进展集中在与数百个用户的小型市场中算法的机理设计和实验验证的理论开发上。相比之下，该项目使数百万用户可以实时匹配并允许现实的不确定偏好。 该方法不仅产生了最大的效率，而且可以保证诸如可信赖和公平之类的结果。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。