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.

公平分配是指在个人、企业、国家或自治主体之间分配资源和任务，以实现公平和经济效率。在分配宝贵和稀缺的医疗设备方面，公平性日益变得至关重要，而在2019冠状病毒病全球大流行期间，公平性的缺失加剧了医疗保健问题。各种各样的现实世界应用程序，如调度、争议解决、医疗保健管理和难民安置，都假设对分配决策有完全的了解，这导致了负面的计算和不可能结果。缓解这些挑战的现有方法反过来又使透明度付出了高昂的代价。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物品提供理论和算法解决方案，为人工智能（AI）和社会公益经济学的宏伟计划做出广泛贡献。这项研究将为制定实用和透明的公平解决方案提供一个新颖而有希望的视角，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。该项目将在一个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，目标是将其范围扩大到广泛的国内和国际受众，并在总体上促进公平和透明度。该项目将制定一个新的框架，以便在分配不可分割的资源方面实现公平和效率，同时使透明度方面的成本降到最低。具体而言，它将在四个相互关联的方面取得进展：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项目时设计算法解决方案；2)公平分配的战略方面，研究透明度要求下代理的行为和策略；3)领域限制，重点是通过规避实现兼容解决方案的不可能结果来开发可处理的解决方案；4)bad和混合物。这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（商品）。此外，本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感知之间的差距。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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