CAREER: A New Theory of Social Choice for More than Two Alternatives: Combining Economics, Statistics, and Computation

职业：两种以上选择的社会选择新理论：结合经济学、统计学和计算

• 批准号: 1453542
• 负责人: Lirong Xia
• 金额: $ 52.5万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453542&HistoricalAwards=false
• 关键词: CAREER; New; Theory; Social; Choice

The proposal aims at generating computational mechanisms that will enable individuals to contribute towards making better collective decisions (e.g., news ranking) including crowdsourcing where aggregation of online noise answers can occur. The proposal brings together ideas from economics, statistics, and computation to expand the capabilities of social choice mechanisms to handle large numbers of alternative choices, to extract ground truth from aggregated preferences, and to address problems where individual agents might not be able to compare some alternatives. In contrast to classical social choice theory, which is limited to the selection between two alternatives, the project proposes a rigorous study of a model for computational choice that will be robust enough for discerning between thousands or even millions of alternatives.The proposal could have a profound impact in the way we build multi-agent systems, search engines and recommender systems. The proposed effort can serve as a catalyst in the growing area of computational social choice, including: (1) Rank aggregation has been used in many fields, involving some high impact applications like ranking of news. However, this problem is far from solved using traditional computational social choice methods because they either only work for two alternatives, require full rankings, does poorly in revealing the ground truth, or are hard to compute. The proposed research will develop new methodologies to overcome these deficiencies by designing objective, robust, and computable social choice mechanisms for rich preferences. (2) Crowdsourcing, whereby online workers' noisy answers are aggregated to produce a better overall answer to some question. This cannot be solved by existing computational social choice techniques as the online workers' answers are often partial orders, workers may manipulate the outcome by providing false answers, and the objective of aggregation is to reveal the true answer. The proposed research will directly tackle these challenges by designing new mechanisms, which are directly applicable to existing systems.

该提案旨在生成计算机制，使个人能够为做出更好的集体决策（例如，新闻排名）做出贡献，其中包括可能发生在线噪音答案聚合的众包。该提案汇集了经济学、统计学和计算的思想，以扩展社会选择机制的能力，以处理大量的替代选择，从汇总偏好中提取基本真理，并解决个体代理可能无法比较某些替代方案的问题。经典的社会选择理论仅限于在两种选择中进行选择，与之相反，该项目提出了对计算选择模型的严格研究，该模型将足够强大，可以在数千甚至数百万种选择中进行识别。这个提议可能会对我们构建多智能体系统、搜索引擎和推荐系统的方式产生深远的影响。所提出的努力可以作为计算社会选择日益增长的领域的催化剂，包括：(1)排名聚合已经在许多领域使用，涉及一些高影响力的应用，如新闻排名。然而，这个问题远远不能用传统的计算社会选择方法来解决，因为它们要么只适用于两种选择，需要完整的排名，在揭示基本真相方面做得很差，要么很难计算。本研究将开发新的方法，通过设计客观、稳健、可计算的丰富偏好的社会选择机制来克服这些缺陷。(2)众包（Crowdsourcing），将在线工作者的各种答案聚合起来，对某些问题给出更好的整体答案。现有的计算社会选择技术无法解决这一问题，因为在线工人的答案往往是偏序的，工人可能通过提供错误的答案来操纵结果，而聚合的目的是揭示真实的答案。拟议的研究将通过设计直接适用于现有系统的新机制来直接解决这些挑战。

项目成果

Learning Mixtures of Random Utility Models

• DOI: 10.1609/aaai.v32i1.11727
• 发表时间: 2018-04
• 作者: Zhibing Zhao;Tristan Villamil;Lirong Xia

Multi resource allocation with partial preferences

• DOI: 10.1016/j.artint.2022.103824
• 发表时间: 2022-11
• 期刊: Artif. Intell.
• 作者: Haibin Wang;Sujoy Sikdar;Xiaoxi Guo;Lirong Xia;Yongzhi Cao;Hanpin Wang

Crowdsourcing Perceptions of Gerrymandering

众包对选区划分的看法

• DOI: 10.1609/hcomp.v10i1.21993
• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
• 作者: Kelly, Benjamin;Kang, Inwon;Xia, Lirong
• 通讯作者: Xia, Lirong

Equitable Allocations of Indivisible Chore

公平分配不可分割的家务活

• 发表时间: 2020
• 期刊: AAMAS Conference proceedings
• 作者: Freeman, Rupert;Sikdar, Sujoy;Vaish, Rohit;Xia, Lirong
• 通讯作者: Xia, Lirong

Learning to Design Fair and Private Voting Rules

• DOI: 10.1613/jair.1.13734
• 发表时间: 2022-11
• 作者: Farhad Mohsin;Ao Liu;Pin-Yu Chen;Francesca Rossi;Lirong Xia