CRII: III: Novel Computational Social Choice Extensions for Highly Distributed Decision-Making Contexts

CRII：III：高度分布式决策环境的新型计算社会选择扩展

基本信息

批准号：
1850355
负责人：
Adolfo Escobedo
金额：
$ 17.43万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-15 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850355&HistoricalAwards=false
关键词：
CRII III Novel Computational Social

项目摘要

Over the last two decades, there has been a growing interest in the aggregation of individual preferences into socially desirable collective choices (e.g., crowdsourced recommendations, online voting), helping to propel the new interdisciplinary field of computational social choice. In many ways, the emphasis on examining whether and how preference aggregation algorithms can be designed to ensure fairness, avoid strategic manipulation, and achieve other socially desirable properties is driven by the largely unchecked prevalence of automated "black-box" decision-making technologies within everyday life. While the rising interest in this new field has resulted in various landmark results, implementation of the more socially beneficial methodologies within modern contexts remains severely limited due to a combination of incompatible assumptions and computational difficulties. This research project will seek to extend the real-world applicability of these robust methodologies by melding socio-theoretical insights, efficient algorithms, and advanced operations research techniques. Accordingly, this novel approach will build interdisciplinary bridges with computer science and expose computational social choice to new audiences. Moreover, through an overarching emphasis on rigorous theoretical underpinnings, the envisioned contributions will address the pressing need to develop and implement interpretable decision-making algorithms. Hence, the outcomes of this project will prospectively have widespread impacts on society. The advances envisioned through the completion of this project will expand the traditional scope of computational social choice, particularly of Kemeny aggregation, which is widely regarded as one of the most robust preference-ranking aggregation frameworks in the literature. The focus of this research project will be on highly distributed decision-making contexts, which are often characterized by large numbers of alternatives, tied (i.e., partial) preferences, errors, and/or incompleteness. This will be accomplished by exploring symbiotic relationships between social choice theory, efficient algorithms, and operations research techniques. Planned research tasks will include: (i) Establishing social choice axioms and properties that different distance measures should satisfy when dealing with partial and incomplete preference rankings; (ii) Constructing mathematical models and decomposition algorithms that take advantage of these insights; and (iii) Exploring the validity and pragmatic implications of these measures via formal statistical methods and benchmark instances of preference data.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.

在过去的二十年中，人们越来越关注将个人偏好聚合为社会理想的集体选择（例如众包推荐、在线投票），这有助于推动计算社会选择的新跨学科领域。在许多方面，对检查是否以及如何设计偏好聚合算法来确保公平、避免策略操纵和实现其他社会所期望的特性的重视是由自动化“黑匣子”决策技术在很大程度上不受控制的流行所驱动的。日常生活。尽管人们对这一新领域的兴趣日益浓厚，取得了各种具有里程碑意义的成果，但由于不相容的假设和计算困难，在现代背景下实施对社会更有益的方法仍然受到严重限制。该研究项目将寻求通过融合社会理论见解、高效算法和先进的运筹学技术来扩展这些强大方法论的实际适用性。因此，这种新颖的方法将与计算机科学建立跨学科桥梁，并向新受众展示计算社会选择。此外，通过全面强调严格的理论基础，预期的贡献将解决开发和实施可解释决策算法的迫切需要。因此，该项目的成果预计将产生广泛的社会影响。通过完成该项目所设想的进步将扩大计算社会选择的传统范围，特别是 Kemeny 聚合，它被广泛认为是文献中最强大的偏好排名聚合框架之一。该研究项目的重点将是高度分布式的决策环境，其特征通常是大量替代方案、绑定（即部分）偏好、错误和/或不完整性。这将通过探索社会选择理论、高效算法和运筹学技术之间的共生关系来实现。计划的研究任务将包括：（i）建立社会选择公理和属性，以处理部分和不完整的偏好排名时不同的距离度量应满足的； (ii) 利用这些见解构建数学模型和分解算法； (iii) 通过正式的统计方法和偏好数据的基准实例探索这些措施的有效性和务实影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。