CRII: RI: Fair, Efficient, and Truthful Resource Allocation in Dynamic Environments

CRII：RI：动态环境中公平、高效、真实的资源分配

基本信息

批准号：
2052488
负责人：
Hadi Hosseini
金额：
$ 13.44万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-16 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052488&HistoricalAwards=false
关键词：
CRII RI Fair Efficient Truthful

项目摘要

Through the integration of artificial intelligence (AI), economics, and computation this project investigates novel solutions for resource allocation in dynamic environments and situations that lack transferable currency. With the advent of online platforms, economic theory emerges as a fundamental approach to promote desirable social properties of efficiency, fairness, and truthfulness in a variety of domains such as shift scheduling, course registration, cloud computing, and crowdsourcing. These new applications are beyond the scope of classical economic theory and market design. Complex scenarios involving changing preferences, dynamic populations, and online items require novel, practical, and scalable solutions. This project tackles a variety of fundamental problems at the intersection AI and economics while enriching the algorithmic and societal understanding of resource allocation in dynamic settings. This contrasts with classical mechanisms that either focus solely on economic aspects of resource allocation in static and offline settings or disregarded social aspects such as fairness. New techniques investigated in this project seek to expand the algorithmic aspects of resource allocation and explore the limits of feasibility for dynamic fair allocation. Advances here can have profound impact in designing efficient mechanisms to allocate resources fairly while incentivizing truthful behavior among the participants. Specifically, the project studies two interconnected components: (1) sequential allocation under uncertainty, by synthesizing models studied in AI with economic theory to investigate, analyze, and create new mechanisms that are fair and discourage strategic manipulation in environments where agents' preferences are evolving (e.g. nurse scheduling and course allocation); and (2) online mechanisms, by employing insights from algorithm design and AI to study fairness and efficiency of allocation mechanisms when agents arrive and depart over time or the availability of items is uncertain (e.g. food bank organizations and crowdsourcing platforms). The new techniques aim to exploit the mathematical framework of decision making under uncertainty as well as axiomatic approaches of algorithmic economic and mechanism design to develop theoretical models for dynamic resource allocation, and employ the multiagent design paradigm to investigate the relation between the well-established mechanisms in fair allocation problems. Ultimately, the findings of this research will lead to the development of robust systems for practical applications in dynamic settings that demand efficient and socially desirable decisions.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.

通过整合人工智能（AI），经济学和计算，该项目研究了在动态环境和缺乏可转移货币的情况下资源分配的新颖解决方案。随着在线平台的出现，经济理论成为一种基本方法，是在各种领域中促进效率，公平和真实性的理想社会特性，例如班次调度，课程注册，云计算和众包。这些新应用超出了古典经济理论和市场设计的范围。复杂的场景涉及改变偏好，动态人群和在线项目需要新颖，实用和可扩展的解决方案。该项目在人工智能和经济学中解决了各种基本问题，同时丰富了动态环境中对资源分配的算法和社会理解。这与古典机制形成鲜明对比，这些机制仅着眼于静态和离线环境中资源分配的经济方面，或者是公平之类的社会方面。该项目中研究的新技术旨在扩大资源分配的算法方面，并探讨动态公平分配可行性的限制。这里的进步可能会在设计有效的机制方面具有深远的影响，以公平地分配资源，同时激励参与者的真实行为。具体而言，该项目研究了两个相互联系的组成部分：（1）在不确定性下进行顺序分配，通过与经济理论中研究的合成模型，以调查，分析和创建新的机制，这些机制是公平而劝阻代理商偏好不断发展的环境中的战略操纵的新机制（例如，培训时间表和课程分配和课程分配）；（2）在线机制，通过采用算法设计和AI的见解来研究代理人随着时间的推移到达或出发时的分配机制的公平和效率，或者不确定物品的可用性（例如，食品银行组织和众包平台）。新技术旨在利用在不确定性下的决策框架以及算法经济和机制设计的公理方法，以开发用于动态资源分配的理论模型，并采用多种设计范式来研究公平分配问题中良好的机制之间的关系。最终，这项研究的发现将导致在需要有效且具有社会性决定的动态环境中实用应用的强大系统开发。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影响的审查标准通过评估来进行评估的。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fair Stable Matchings Under Correlated Preferences (Student Abstract)

相关偏好下的公平稳定匹配（学生摘要）

DOI：
发表时间：
2021
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Brilliantova, Angelina;Hosseini, Hadi
通讯作者：
Hosseini, Hadi

Fairness Does Not Imply Satisfaction (Student Abstract)

公平并不意味着满意（学生摘要）

DOI：
10.1609/aaai.v34i10.7228
发表时间：
2020
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Searns, Andrew;Hosseini, Hadi
通讯作者：
Hosseini, Hadi

Graphical House Allocation

图形房屋分配

DOI：
发表时间：
2023
期刊：
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems
影响因子：
0
作者：
Hosseini, Hadi;Payan, Justin;Sengupta, Rik;Vaish, Rohit;Viswanathan, Vignesh
通讯作者：
Viswanathan, Vignesh

Ordinal Maximin Share Approximation for Goods

DOI：
10.1613/jair.1.13317
发表时间：
2021-09
期刊：
ArXiv
影响因子：
0
作者：
Hadi Hosseini;Andrew Searns;Erel Segal-Halevi
通讯作者：
Hadi Hosseini;Andrew Searns;Erel Segal-Halevi

Ordinal Maximin Share Approximation for Chores

家务劳动的序数最大最小份额近似

DOI：
发表时间：
2022
期刊：
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
影响因子：
0
作者：
Hosseini, Hadi;Searns, Andrew;Segal-Halevi, Erel
通讯作者：
Segal-Halevi, Erel