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

在过去的二十年里，人们越来越感兴趣的是将个人偏好聚集成社会上可取的集体选择（例如，众包推荐，在线投票），有助于推动计算社会选择的新跨学科领域。在许多方面，强调研究偏好聚合算法是否以及如何设计以确保公平性，避免战略操纵，并实现其他社会期望的属性是由日常生活中自动化“黑箱”决策技术的普遍存在所驱动的。虽然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，在现代背景下实施更有益于社会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的实际应用。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的受众。此外，通过全面强调严格的理论基础，设想的贡献将解决迫切需要开发和实施可解释的决策算法。因此，该项目的成果将对社会产生广泛的影响。 通过完成这个项目所设想的进步将扩大传统的计算社会选择的范围，特别是Kemeny聚合，这被广泛认为是文献中最强大的偏好排名聚合框架之一。这个研究项目的重点将是高度分布式的决策环境，其特点往往是大量的替代品，捆绑（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：（一）建立社会选择公理和属性，不同的距离措施应满足处理部分和不完整的偏好排名;（二）构建数学模型和分解算法，利用这些见解;以及（iii）通过正式的统计方法和偏好数据的基准实例来探索这些措施的有效性和实用意义。反映了NSF的法定使命，并已被认为是值得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的支持。

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.