Collaborative Research: NSF-CSIRO: Fair Sequential Collective Decision-Making

合作研究：NSF-CSIRO：公平顺序集体决策

• 批准号: 2303000
• 负责人: Lirong Xia
• 金额: $ 30万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303000&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; CSIRO; Fair

This project aims to develop (artificial intelligence) AI-powered approaches to address challenging societal problems, such as dealing with droughts, infectious diseases, and environmentally harmful emissions. This project engages specific questions in these areas, such as: How can one effectively allocate water resources to increase agricultural drought resilience during drought seasons? How can one effectively determine when and where to construct hydrogen or electric vehicle refueling stations to encourage citizens to adopt these technologies to lower emissions? How can one effectively distribute vaccines and other medical supplies daily to enhance response to infectious diseases during pandemics? These problems belong to a class of classical and important problems in sequential collective decision-making. While sequential decision-making and collective decision-making have been studied previously, decision-making problems that are simultaneously sequential and collective are poorly understood, especially for specific domains such as resource allocation and when combined with goals such as responsibility and equitability. The overarching project goal is to establish theoretical and algorithmic foundations for responsible and equitable AI-powered sequential, collective decision-making. It also seeks to ensure that sequences of decisions satisfy multiple objectives and make appropriate trade-offs between short and long-term rewards subject to fairness criteria. The proposed research will lead to efficient and fair solutions to our social good and use-inspired applications in drought resilience, towards net zero, and infectious disease resilience. The project will achieve its goals by focusing on three interconnected challenges, leveraging a wide range of techniques from AI, economics, and operation research. First, the challenge of fair multi-objective collective decision-making for a single time period subject to multiple objectives and fairness criteria. Second, explore fair sequential multi-objective collective decision-making that addresses trade-offs between immediate and long-term efficiency and fairness. Finally, understand the strategic aspects of fair multi-objective collective decision-making in collaboration with stakeholders, who provide information that facilitates the process.This is a joint project between United States and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).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.

该项目旨在开发（人工智能）人工智能驱动的方法来解决具有挑战性的社会问题，例如应对干旱、传染病和对环境有害的排放。该项目涉及这些领域的具体问题，例如：如何有效配置水资源以提高干旱季节农业抗旱能力？如何有效地确定何时何地建设氢能或电动汽车加氢站，以鼓励公民采用这些技术来降低排放？如何每天有效地分发疫苗和其他医疗用品，以加强对大流行期间传染病的应对？这些问题属于序贯集体决策中的一类经典且重要的问题。虽然顺序决策和集体决策之前已经被研究过，但同时顺序和集体的决策问题却知之甚少，特别是对于资源分配等特定领域以及与责任和公平等目标相结合时。项目的总体目标是为负责任和公平的人工智能驱动的顺序集体决策建立理论和算法基础。它还力求确保决策序列满足多个目标，并在符合公平标准的情况下在短期和长期奖励之间进行适当的权衡。拟议的研究将为我们的社会公益和干旱抵御能力、实现净零排放和传染病抵御能力等方面的应用提供高效和公平的解决方案。该项目将通过关注三个相互关联的挑战，利用人工智能、经济学和运筹学的广泛技术来实现其目标。首先，在单一时间段内根据多个目标和公平标准进行公平的多目标集体决策的挑战。其次，探索公平序贯的多目标集体决策，解决近期和长期效率与公平之间的权衡。最后，与利益相关者合作了解公平多目标集体决策的战略方面，利益相关者提供促进这一过程的信息。这是美国和澳大利亚研究人员之间的联合项目，由负责任和公平人工智能合作机会资助美国 NSF 和澳大利亚联邦科学与工业研究组织 (CSIRO)。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，认为值得支持。

项目成果

Average Envy-freeness for Indivisible Items

不可分割物品的平均无嫉妒度

• DOI: 10.1145/3617694.3623229
• 发表时间: 2023-10
• 期刊: Proceedings of EAAMO
• 作者: Han, Qishen;Tao, Biaoshuai;Xia, Lirong
• 通讯作者: Xia, Lirong