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

这个I-Corps项目的更广泛影响/商业潜力侧重于开发新技术，以便在公平和可信的约束下实现有效的双边匹配。目前有许多技术可以实现供需的双边匹配。那些公平可信的评估往往缺乏商业应用的效率，缺乏足够的性能规格，而那些具有理想效率的评估对于大规模市场来说在计算上过于昂贵。拟议的计划在项目最初的应用重点中探索实施和商业化机会，中小型企业在制造业。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和降低价格。此外，这项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颠覆性影响。发达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项目的前提是，最先进的机器学习和经济原理的交叉会导致颠覆性创新。该项目的技术提供了一种与过去60年开发的技术完全不同的双面匹配方法。这个项目追求一个数据驱动的解决方案来解决一个动态的双边匹配问题。该项目的先前进展主要集中在机制设计的理论发展和算法在拥有数百个用户的小规模市场中的实验验证。相比之下，该项目为数百万用户提供了一种广泛可扩展的方法，可以实时匹配并允许现实的不确定偏好。这种方法不仅可以最大限度地提高效率，还可以保证可信度和公平性等结果。该奖项反映了美国国家科学基金会的法定使命，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。

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.