Collaborative Research: RI: Medium: Transparent Fair Division of Indivisible Items

合作研究：RI：媒介：不可分割项目的透明公平划分

基本信息

批准号：
2106983
负责人：
Lirong Xia
金额：
$ 62.19万
依托单位：
Rensselaer Polytechnic Institute
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：
2021-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106983&HistoricalAwards=false
关键词：
Collaborative Research RI Medium Transparent

项目摘要

Fair division deals with the distribution of resources and tasks among different parties, e.g., individuals, firms, nations, or autonomous agents, with the goal of achieving fairness and economic efficiency. Fairness has increasingly become crucial in distributing precious and scarce medical equipment, and its absence has exacerbated healthcare issues during the COVID-19 global pandemic. A wide variety of real-world applications such as scheduling, dispute resolution, healthcare management, and refugee settlement assume complete knowledge about allocation decisions, which gives rise to negative computational and impossibility results. The existing approaches to mitigate these challenges, in turn, impose a high cost on transparency. The broad goal of this project is to provide theoretical and algorithmic solutions for fair allocation of indivisible items in practical, large-scale settings, as a broad contribution to the grand scheme of artificial intelligence (AI) and economics for social good. This research will offer a novel and promising perspective for developing practical and transparent fair solutions while providing a systematic investigation on the perceived fairness of allocation mechanisms that are applicable to societies at large. This project will integrate and develop algorithmic solutions for transparent fair division in a publicly available software system with the goal of extending its reach--and in general promoting fairness and transparency--to a broad national and international audience. This project will develop a new framework for achieving fairness and efficiency in the allocation of indivisible resources with minimum cost on transparency. Specifically, it will make progress in four interconnected dimensions: 1) Tradeoffs between transparency, fairness, and efficiency, that aim at analyzing the compatibility of the properties and devising algorithmic solutions when allocating indivisible items, 2) Strategic aspects of fair division, that investigates agents' behavior and strategies under transparency requirements, 3) Domain restriction, that focuses on developing tractable solutions by circumventing the impossibility results in achieving compatible solutions, and 4) Bads and mixtures, that extend the transparency and fairness framework to include desirable (goods) and undesirable items (bads). Furthermore, this research plans to close the current gap between theoretical foundations of fairness and the perception of fairness through a series of comprehensive empirical evaluations.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.

公平分配涉及不同主体（例如个人、企业、国家或自治主体）之间的资源和任务分配，以实现公平和经济效率的目标。在分配珍贵和稀缺的医疗设备时，公平性日益变得至关重要，而在 COVID-19 全球大流行期间，公平性的缺失加剧了医疗保健问题。各种现实世界的应用程序，例如调度、争议解决、医疗保健管理和难民安置，都假设完全了解分配决策，这会导致负面的计算结果和不可能的结果。缓解这些挑战的现有方法反过来又给透明度带来了高昂的成本。该项目的总体目标是为在实际的大规模环境中公平分配不可分割的物品提供理论和算法解决方案，作为对人工智能（AI）和社会公益经济学的宏伟计划的广泛贡献。这项研究将为开发实用且透明的公平解决方案提供新颖且有前景的视角，同时对适用于整个社会的分配机制的感知公平性进行系统调查。该项目将在公开可用的软件系统中集成和开发透明公平分配的算法解决方案，其目标是将其范围扩大到广大的国内和国际受众，并总体上促进公平和透明度。该项目将开发一个新框架，以最小的透明度成本实现不可分割资源分配的公平和效率。具体来说，它将在四个相互关联的维度上取得进展：1）透明度、公平性和效率之间的权衡，旨在分析属性的兼容性并在分配不可分割的项目时设计算法解决方案；2）公平分配的战略方面，即调查透明度要求下代理人的行为和策略，3) 领域限制，重点是通过规避实现兼容解决方案的不可能性结果来开发易于处理的解决方案，以及 4) 坏处和混合物，扩大透明度和公平框架包括理想的（好的）和不需要的（坏的）。此外，这项研究计划通过一系列全面的实证评估来缩小目前公平理论基础与公平认知之间的差距。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力价值和更广泛的评估进行评估，认为值得支持。影响审查标准。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

First-Choice Maximality Meets Ex-ante and Ex-post Fairness

首选最大化满足事前事后公平性

DOI：
发表时间：
2023-08
期刊：
IJCAI
影响因子：
0
作者：
Guo, Xiaoxi;Sikdar, Sujoy;Xia, Lirong;Cao, Yongzhi;Wang, Hanpin
通讯作者：
Wang, Hanpin

Multi resource allocation with partial preferences

具有部分偏好的多资源分配

DOI：
10.1016/j.artint.2022.103824
发表时间：
2023-01
期刊：
Artificial Intelligence
影响因子：
14.4
作者：
Wang, Haibin;Sikdar, Sujoy;Guo, Xiaoxi;Xia, Lirong;Cao, Yongzhi;Wang, Hanpin
通讯作者：
Wang, Hanpin

The Impact of a Coalition: Assessing the Likelihood of Voter Influence in Large Elections

联盟的影响：评估选民在大型选举中影响的可能性