RI: Small: Computational Social Choice: For the People

RI：小：计算社会选择：为了人民

• 批准号: 2024287
• 负责人: Ariel Procaccia
• 金额: $ 24.32万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024287&HistoricalAwards=false
• 关键词: RI; Small; Computational; Social; Choice

The field of social choice theory deals with aggregating the preferences or opinions of individuals towards a collective decision; voting is a paradigmatic example. This rich space of problems has long been studied in economics and mathematics, leading to a slew of striking theoretical results. But real-world applications have been sparse. From the AI viewpoint, the study of computational social choice is seen by many researchers as a central component in the effort to build the foundations of multiagent systems. This research extends this theoretical work in computational social choice to enable people to make joint decisions. This project aims to put human decision making at the center of computational social choice, while leveraging the very approaches and techniques developed for voting in multiagent systems. The research plan is directly motivated by the not-for-profit website RoboVote.org, which enables people to implement whichever voting methods appear to be best based on analysis and empirical evidence. This project will realize the full potential of RoboVote for education, outreach, and societal impact with the aim of transforming the way people make group decisions in a wide range of applications.The specific challenges that will be tackled are divided into two subsets, corresponding to the two fundamentally different types of polls currently modeled on RoboVote. 1. Subjective preferences: Preferences are subjective when the desirability of each alternative is a matter of taste. RoboVote aggregates subjective rankings by assuming that voters have latent utilities for the alternatives, and selecting an outcome that maximizes the sum of utilities by using the reported rankings as a proxy for those utilities. An immediate gap that must be addressed is that the approach does not extend to the case where the outcome is a ranking. A second, far-reaching challenge is to rethink the way voters express their preferences, in order to obtain more useful information regarding their actual utility functions while keeping the cognitive burden low. Finally, the project includes a study of the axiomatic properties of optimal aggregation methods in the foregoing framework. 2. Objective opinions: In this scenario, some alternatives are objectively better than others, but this objective comparison is not known to voters. The solutions deployed on RoboVote aim to handle worst-case noise. The research aims to create and test algorithms that improve upon naïve approaches, especially by building on synergistic advances in the design of fixed-parameter tractable algorithms.

社会选择理论领域涉及将个人的偏好或意见汇总到集体决策中，这是一个典型的例子，这一丰富的问题​​领域长期以来一直在经济学和数学领域进行研究，产生了一系列引人注目的理论结果。从人工智能的角度来看，计算社会选择的研究被许多研究人员视为构建多智能体系统基础的核心组成部分。选择使人们能够联合起来该项目旨在将人类决策置于计算社会选择的中心，同时利用为多智能体系统中投票而开发的方法和技术。该研究计划是由非营利网站 RoboVote.org 直接推动的。使人们能够实施基于分析和经验证据的最佳投票方法。该项目将充分发挥 RoboVote 在教育、推广和社会影响方面的潜力，旨在广泛改变人们做出群体决策的方式。应用范围。具体的挑战所要解决的问题分为两个子集，对应于当前在 RoboVote 上建模的两种根本不同类型的民意调查 1. 主观偏好：当每个选项的可取性取决于选民的喜好时，偏好是主观的。具有替代方案的潜在效用，并通过使用报告的排名作为这些效用的代理来选择最大化效用总和的结果。必须解决的一个直接差距是该方法不能扩展到以下情况：第二个影响深远的挑战是重新思考选民表达偏好的方式，以便在保持较低的认知负担的同时获得更多有用的信息。上述框架中最优聚合方法的公理属性。 客观意见：在这种情况下，某些替代方案客观上优于其他替代方案，但选民不知道这种客观比较是在 RoboVote 上部署的解决方案旨在处理最坏的情况。噪音。研究旨在创建和测试改进朴素方法的算法，特别是通过在固定参数易处理算法的设计中建立协同进步。

项目成果

Axioms for Learning from Pairwise Comparisons

从成对比较中学习的公理

• 发表时间: 2020-01
• 期刊: NeurIPS
• 作者: Noothigattu, Ritesh;Peters, Dominik;Procaccia, Ariel D.
• 通讯作者: Procaccia, Ariel D.

Paradoxes in Fair Machine Learning

公平机器学习中的悖论

• 发表时间: 2019-01
• 期刊: NeurIPS'19
• 作者: Goelz, P;Kahng, A;Procaccia, AD
• 通讯作者: Procaccia, AD

District-Fair Participatory Budgeting

地区公平参与式预算

• 发表时间: 2021-01
• 期刊: AAAI
• 作者: Hershkowitz, D. Ellis;Kahng, Anson;Peters, Dominik;Procaccia, Ariel D.
• 通讯作者: Procaccia, Ariel D.

Loss Functions, Axioms, and Peer Review

损失函数、公理和同行评审

• 发表时间: 2021-01
• 期刊: Journal of artificial intelligence research
• 影响因子: 5
• 作者: Noothigattu, Ritesh;Shah, Nihar B.;Procaccia, Ariel D.
• 通讯作者: Procaccia, Ariel D.

Liquid Democracy: An Algorithmic Perspective

流动民主：算法视角

• DOI: 10.1609/aaai.v32i1.11468
• 发表时间: 2018-04-25
• 期刊: ArXiv
• 作者: Anson Kahng;Simon Mackenzie;Ariel D. Procaccia
• 通讯作者: Ariel D. Procaccia