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项目更广泛的影响/商业潜力侧重于开发新技术，以在公平和可信的约束下实现高效的双边匹配。目前存在多种用于供应和需求的双边匹配的技术。那些公平可信的评估对于商业应用来说往往缺乏效率，并且缺乏足够的性能规范，而那些具有理想效率的评估对于大规模市场来说计算成本太高。拟议的计划探讨了该项目最初的应用重点是制造业中小型企业的实施和商业化机会。所提出的技术具有广泛的应用潜力，可以大大减少寻找合适供应商的时间并降低价格。此外，该技术的长期发展可能会对呼叫中心和学生辅导网站等市场产生破坏性影响。所开发的技术还可以改善供应链，更好地分配稀缺资源，例如疫苗和药品，同时确保匹配过程中的社会效率和公平。这个I-Corps项目基于以下前提：最先进的机器学习和经济原理带来颠覆性创新。该项目的技术提供了一种与过去六十年开发的技术截然不同的双面匹配方法。该项目寻求一种数据驱动的解决方案来解决动态两侧匹配问题。该项目前期的进展主要集中在数百用户的小规模市场中机制设计的理论发展和算法的实验验证。相比之下，该项目提供了一种可广泛扩展的方法，可以实时匹配数百万用户，并允许现实的不确定偏好。 该方法不仅能产生最大的效率，还能保证可信性和公平性等结果。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。