CAREER: Scalable and Robust Dynamic Matching Market Design

职业：可扩展且稳健的动态匹配市场设计

基本信息

批准号：
1846237
负责人：
John Dickerson
金额：
$ 55万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-02-01 至 2024-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846237&HistoricalAwards=false
关键词：
CAREER Scalable Robust Dynamic Matching

项目摘要

Markets are systems that empower interested parties -- humans, firms, governments, or autonomous agents -- to exchange goods, services, and information. Due to logistical or societal constraints, many markets, e.g., school choice, rideshare, medical residency, advertising, cadaveric organ allocation, online labor, public housing, refugee placement, as well as barter markets such as kidney exchange, cannot rely solely on prices to match supply and demand. This project will develop connections between artificial intelligence (AI) and matching market theory and practice. Specifically, it will create practical methods for the design and analysis of dynamic matching markets operating under various forms of uncertainty, with complex goals, and under objectives that take into account stakeholders' value judgments in a principled way. This research will improve existing systems as well as enable new markets. It will directly address problems in real-world matching systems such as kidney exchange and will lead to improvements in fairness, economic efficiency, and ethical alignment of objectives in fielded kidney allocation systems and other markets. This project will also lay the groundwork for a redesign of the computer science faculty and post-doc job matching market; and it will begin developing principled systems to promote diversity in university admissions, as well as quantitative and technical hiring. Open source software solutions developed as a byproduct of this research will be made available to the public, and results will be communicated with educators.This project will substantially increase the breadth of matching market design, traditionally addressed via microeconomic theory, by developing scalable AI-based methods for designing and analyzing these markets. In turn, matching markets will serve as impetus and inspiration for the development of principled and scalable general optimization-based algorithms for decision making under uncertainty, as well as general AI-based methods for alignment of systems with elicited stakeholder value judgments. The research will make progress in the following three directions:1) Principled approaches to managing short-term uncertainty. Often, decisions must be made before all information about the environment is revealed. This project will take an AI-based approach to decision making under uncertainty; specifically, it will design and create principled methods to learn (at some cost) about the environment that tie into novel methods for computing robust matching policies.2) Learning to balance fairness and efficiency through dynamic matching. Many matching markets are dynamic: participants arrive and depart over time, and relationships between them evolve in predictable ways. Via reinforcement learning and approximate dynamic programming, this project will develop principled and practical ways to learn matching policies that provide both economic efficiency gains alongside fairness and/or diversity-promoting guarantees.3) Alignment with human value judgments. AI-based matching methods require an objective to optimize. Defining that objective involves a feedback loop between human stakeholders---each with their own value judgments---and automated systems. This research will meld techniques for aggregating value judgments of human stakeholders into automated matching and resource allocation systems.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.

市场是授权感兴趣的各方（人类，公司，政府或自治代理）来交换商品，服务和信息的系统。由于后勤或社会限制，许多市场，例如学校的选择，乘车，医疗居住，广告，尸体器官分配，在线劳动力，公共住房，难民安置，以及肾脏交易所等易货市场，不能完全依靠价格来匹配供应和需求。该项目将在人工智能（AI）与匹配的市场理论与实践之间建立联系。具体而言，它将创建实用方法，以设计和分析在各种形式的不确定性，复杂目标以及目标下，以原则性的方式考虑利益相关者的价值判断的目标。这项研究将改善现有系统，并启用新市场。它将直接解决现实世界中的匹配系统（例如肾脏交换）的问题，并将导致在现场肾脏分配系统和其他市场中目标的公平，经济效率和道德一致性的提高。该项目还将为重新设计计算机科学教师和DOC后工作匹配市场的基础奠定基础；它将开始开发有原则的系统，以促进大学录取的多样性以及定量和技术招聘。开发软件解决方案将作为本研究的副产品开发，将向公众提供，并将与教育工作者传达结果。该项目将通过开发基于可扩展的AI基于AI的方法来设计和分析这些市场，从而大大提高匹配市场设计的广度，传统上通过微观经济理论解决。反过来，匹配的市场将成为开发不确定性下的决策的原则性和可扩展性一般优化算法的动力和灵感，以及基于AI的一般方法，用于对具有引起利益相关者价值判断的系统的对准。该研究将在以下三个方向上取得进展：1）管理短期不确定性的原则方法。通常，必须在揭示有关环境的所有信息之前做出决定。该项目将采用基于AI的基于不确定性的决策方法；具体而言，它将设计并创建有原则的方法，以学习（以某种成本）将环境学习到计算可靠匹配策略的新方法的环境。2）学习通过动态匹配来平衡公平和效率。许多匹配的市场都是动态的：参与者随着时间的推移而到达并离开，它们之间的关系以可预测的方式发展。通过强化学习和近似动态编程，该项目将开发有原则和实用的方法来学习匹配政策，从而提供经济效率提高以及公平性和/或促进多样性的保证。3）与人类价值判断保持一致。基于AI的匹配方法需要一个目标才能优化。定义该目标涉及人类利益相关者之间的反馈循环 - 每个人都有自己的价值判断----自动化系统。这项研究将融合到将人类利益相关者汇总到自动匹配和资源分配系统中的价值判断的技术。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和更广泛影响的评估评估的评估来支持的。

项目成果

期刊论文数量（39）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

DOI：
发表时间：
2020-06
期刊：
ArXiv
影响因子：
0
作者：
Avi Schwarzschild;Micah Goldblum;Arjun Gupta;John P. Dickerson;T. Goldstein
通讯作者：
Avi Schwarzschild;Micah Goldblum;Arjun Gupta;John P. Dickerson;T. Goldstein

Fair Clustering Under a Bounded Cost

DOI：
发表时间：
2021-06
期刊：
ArXiv
影响因子：
0
作者：
Seyed-Alireza Esmaeili;Brian Brubach;A. Srinivasan;John P. Dickerson
通讯作者：
Seyed-Alireza Esmaeili;Brian Brubach;A. Srinivasan;John P. Dickerson

Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting

规划公平分配：不安定强盗环境中的概率公平

DOI：
10.1145/3580305.3599467
发表时间：
2023
期刊：
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Herlihy, Christine;Prins, Aviva;Srinivasan, Aravind;Dickerson, John P.
通讯作者：
Dickerson, John P.

Adapting a kidney exchange algorithm to align with human values