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

• 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

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.

公平分工涉及资源和任务在不同各方之间的分配，例如个人、公司、国家或自治机构，目标是实现公平和经济效率。在分配珍贵而稀缺的医疗设备方面，公平日益变得至关重要，而公平的缺失加剧了新冠肺炎全球疫情期间的医疗问题。各种各样的现实世界应用程序，如调度、争端解决、医疗保健管理和难民解决方案，都假设完全了解分配决策，这会导致负面的计算和不可能的结果。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。该项目的广泛目标是为在实际、大规模的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)和经济社会公益的宏伟计划的广泛贡献。这项研究将为制定实用和透明的公平解决办法提供一个新颖和有希望的视角，同时对适用于整个社会的分配机制的公平性进行系统调查。该项目将在一个公开可用的软件系统中整合和开发透明公平划分的算法解决方案，目的是扩大其覆盖范围--并在总体上促进公平和透明度--面向广大的国内和国际受众。该项目将制定一个新的框架，以最小的透明度代价实现分配不可分割的资源的公平和效率。具体地说，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)公平划分的战略方面，调查透明度要求下代理人的行为和策略；3)领域限制，侧重于通过绕过不可能的结果来开发易处理的解决方案，以获得兼容的解决方案；以及4)坏和混合，将透明度和公平性框架扩展到包括可取的(商品)和不可取的项目(坏)。此外，这项研究计划通过一系列全面的实证评估来弥合公平的理论基础和公平观念之间的差距。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

首选最大化满足事前事后公平性

• 发表时间: 2023
• 期刊: IJCAI
• 作者: Guo, Xiaoxi;Sikdar, Sujoy;Xia, Lirong;Cao, Yongzhi;Wang, Hanpin
• 通讯作者: Wang, Hanpin

Multi resource allocation with partial preferences

• DOI: 10.1016/j.artint.2022.103824
• 发表时间: 2022-11
• 期刊: Artif. Intell.
• 作者: Haibin Wang;Sujoy Sikdar;Xiaoxi Guo;Lirong Xia;Yongzhi Cao;Hanpin Wang

Learning to Design Fair and Private Voting Rules

• DOI: 10.1613/jair.1.13734
• 发表时间: 2022-11
• 作者: Farhad Mohsin;Ao Liu;Pin-Yu Chen;Francesca Rossi;Lirong Xia

Convergence of Multi-Issue Iterative Voting under Uncertainty

• DOI: 10.48550/arxiv.2301.08873
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Joshua Kavner;R. Meir;Francesca Rossi;Lirong Xia

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

• DOI: 10.1145/3580507.3597731
• 发表时间: 2023
• 期刊: ACM
• 作者: Xia, Lirong