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，人们对将个体偏好汇总到社会期望的集体选择（例如，众包建议，在线投票）中一直引起人们的兴趣，有助于推动新的计算社会选择跨学科领域。在许多方面，强调研究偏好聚合算法是否可以设计以确保公平性，避免战略性操纵并实现其他具有社会期望的特性，这是由于在日常生活中自动化“ Black-Box”决策技术的很大程度上未经检查的普遍性的驱动。尽管对这个新领域的兴趣不断提高导致各种具有里程碑意义的结果，但由于不兼容的假设和计算困难的结合，在现代背景下实施更有益的方法论仍然受到严重限制。该研究项目将寻求通过融合社会理论见解，有效的算法和高级操作研究技术来扩展这些强大方法论的现实适用性。因此，这种新颖的方法将与计算机科学建立跨学科的桥梁，并将计算社会选择暴露给新受众。此外，通过总体上强调严格的理论基础，设想的贡献将解决发展和实施可解释的决策算法的紧迫需求。因此，该项目的结果将对社会产生广泛的影响。通过完成该项目的完成所设想的进步将扩大传统的计算社会选择范围，尤其是Kemeny Contregation，该范围被广泛认为是文献中最强大的偏好级聚合框架之一。该研究项目的重点将放在高度分布的决策环境上，这些环境通常以大量替代方案（即部分）偏好，错误和/或不完整为特征。这将通过探索社会选择理论，有效算法和操作研究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，在处理部分和不完整的偏好排名时，不同距离措施应满足；（ii）构建利用这些见解的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含义。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。