AF: Medium:Algorithmic Market Design

AF：媒介：算法市场设计

• 批准号: 2312156
• 负责人: Itai Ashlagi
• 金额: $ 120万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312156&HistoricalAwards=false
• 关键词: AF; Medium; Algorithmic; Market; Design

This project investigates the development of algorithmic foundations to address challenges in real-world markets in which matching is an essential activity. The proposal addresses timely challenges in organ allocation, kidney exchange, student assignment systems, and other matching marketplaces, enriching the mathematically grounded algorithms for resource allocation and matching. The project serves the national interest by promoting scientific progress, advancing market design knowledge, addressing equity and fairness in resource allocation, and enhancing societal welfare. The project will also train undergraduate and graduate students.The research project will address the following areas: algorithmic questions in labor markets, student assignment systems, organ allocation challenges, and algorithmic market design questions in transportation. The project will study the role of information and learning, preference discovery, and equity in matching markets. The project will explore the interaction between algorithms and information towards reducing congestion in entry-level labor markets. In organ allocation, the team will design algorithms for allocating hard-to-place organs, addressing novel algorithmic questions related to online matching and trade-offs between utilization and welfare. The project will also investigate token systems for incentivizing participation of hospitals in kidney exchange platforms. Lastly, it will tackle matching and pricing questions in transportation markets, developing a unifying framework for congestion pricing and carpooling. By integrating algorithms and application understanding, the project seeks to develop grounded solutions for resource allocation and matching, making it both intellectually and practically impactful.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.

该项目研究算法基础的开发，以应对现实世界市场中的挑战，其中匹配是一项重要活动。该提案及时解决了器官分配、肾脏交换、学生分配系统和其他匹配市场方面的挑战，丰富了资源分配和匹配的数学算法。该项目通过促进科学进步、提升市场设计知识、解决资源配置的公平性和公平性以及提高社会福利来服务于国家利益。该项目还将培训本科生和研究生。该研究项目将解决以下领域：劳动力市场中的算法问题、学生分配系统、器官分配挑战以及交通中的算法市场设计问题。该项目将研究信息和学习、偏好发现以及公平在匹配市场中的作用。该项目将探索算法和信息之间的相互作用，以减少入门级劳动力市场的拥堵。 在器官分配方面，该团队将设计用于分配难以放置的器官的算法，解决与在线匹配以及利用率和福利之间的权衡相关的新颖算法问题。该项目还将研究激励医院参与肾脏交换平台的代币系统。最后，它将解决交通市场的匹配和定价问题，开发拥堵定价和拼车的统一框架。通过整合算法和应用理解，该项目旨在开发用于资源分配和匹配的扎根解决方案，使其在智力和实践上都具有影响力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。