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

公平分配涉及不同参与方之间的资源和任务分配，例如，个人、公司、国家或自治代理人，以实现公平和经济效率为目标。公平在分配珍贵和稀缺的医疗设备方面越来越重要，而在COVID-19全球大流行期间，公平的缺失加剧了医疗保健问题。各种各样的现实世界的应用，如调度，纠纷解决，医疗保健管理和难民安置假设完整的知识分配决策，这会产生负面的计算和不可能的结果。缓解这些挑战的现有办法反过来又使透明度付出了高昂的代价。该项目的主要目标是为在实际的大规模环境中公平分配不可分割的项目提供理论和算法解决方案，作为对人工智能（AI）和社会公益经济学宏伟计划的广泛贡献。这项研究将提供一个新的和有前途的角度，制定切实可行的和透明的公平的解决方案，同时提供了一个系统的调查分配机制，适用于广大社会的感知公平。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分配的算法解决方案，目标是将其范围扩大到广大的国家和国际受众，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，以最低的透明度成本实现分配不可分割资源的公平和效率。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的项目时设计算法解决方案，2）公平划分的策略方面，调查代理人在透明度要求下的行为和策略，3）域限制，4）坏和混合，扩展了透明和公平框架，包括可取的（好的）和不可取的（坏的）项目。此外，该研究计划通过一系列综合性的实证评估，缩小公平性的理论基础与公平性感知之间的差距。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.