RI: Small: Protecting Social Choice Mechanisms from Malicious Influence

RI：小：保护社会选择机制免受恶意影响

• 批准号: 1903207
• 负责人: Yevgeniy Vorobeychik
• 金额: $ 36.82万
• 依托单位: Washington 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: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903207&HistoricalAwards=false
• 关键词: RI; Small; Protecting; Social; Choice

Vulnerability of social choice mechanisms to malicious influence is a long-standing concern. An increasingly important means of malicious influence involves digital media platforms through the use of fake or highly misleading information. The net effect of such influence is to change the attitudes of mechanism participants about the relative importance of issues, as well as their perception of candidates and their views and, as a consequence, change social choice outcomes. This proposal aims to develop systematic quantitative and algorithmic approaches for studying control of social choice mechanisms when manipulation targets perceptions about and views on issues. Consequently, this research has the potential to significantly contribute to the broader interdisciplinary study of vulnerability of social choice mechanisms to malicious influence. Specifically, this research will investigate a novel model of control of social choice mechanisms which builds on the spatial theory of voting in which voters' preferences over candidates are generated based on their relative similarity on issues. In this model, the primary target of manipulation is perception of issues, both in terms of their relative importance, as well as perception of candidates' views. Algorithmically, this opens several new classes of problems with little prior research, such as the problem of issue selection, where the malicious party may select which issues are most salient, or opinion manipulation where a malicious actor aims to mislead mechanism participants about where the candidates stand on issues. Conceptually, this problem has a more direct connection to common means of subversion of social choice mechanism in reality, such as advertising and campaigning, both on social and conventional media. Furthermore, this project will study the dual problem of protecting social choice mechanisms in the spatial model, providing a series of novel modeling and algorithmic contributions for this problem.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.

社会选择机制容易受到恶意影响是一个长期存在的问题。一种日益重要的恶意影响手段涉及数字媒体平台，通过使用虚假或极具误导性的信息。这种影响的净效果是改变机制参与者对问题相对重要性的态度，以及他们对候选人及其观点的看法，从而改变社会选择结果。本提案旨在开发系统的定量和算法方法来研究当操纵目标是对问题的看法和观点时对社会选择机制的控制。因此，这项研究有可能为更广泛的跨学科研究社会选择机制对恶意影响的脆弱性做出重大贡献。具体而言，本研究将探讨一种新的社会选择机制控制模型，该模型建立在投票的空间理论基础上，选民对候选人的偏好是基于他们在问题上的相对相似性产生的。在这个模型中，操纵的主要目标是对问题的感知，既包括对其相对重要性的感知，也包括对候选人观点的感知。从算法上讲，这开启了几类新的问题，之前的研究很少，比如问题选择问题，恶意方可能会选择哪些问题最突出，或者意见操纵，恶意行为者的目的是误导机制参与者关于候选人在问题上的立场。从概念上讲，这一问题与现实中颠覆社会选择机制的常见手段有更直接的联系，例如在社会和传统媒体上的广告和竞选活动。此外，本项目将研究空间模型中保护社会选择机制的双重问题，为该问题提供一系列新颖的建模和算法贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Altruism Design in Networked Public Goods Games

• DOI: 10.24963/ijcai.2021/69
• 发表时间: 2021-05
• 作者: Sixie Yu;D. Kempe;Yevgeniy Vorobeychik

Manipulating Elections by Changing Voter Perceptions

通过改变选民的看法来操纵选举

• DOI: 10.24963/ijcai.2022/79
• 发表时间: 2022
• 期刊: International Joint Conference on Artificial Intelligence
• 作者: Wu, Junlin;Estornell, Andrew;Kong, Lecheng;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

Inducing Equilibria in Networked Public Goods Games through Network Structure Modification

通过网络结构修改引入网络公共物品博弈的均衡

• 发表时间: 2020
• 期刊: International Conference on Autonomous Agents and Multiagent Systems (AAMAS
• 作者: Kempe, David;Yu, Sixie;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

Adversarial Link Prediction in Spatial Networks

• DOI: 10.5555/3545946.3598846
• 发表时间: 2023
• 作者: M. T. Godziszewski;Yevgeniy Vorobeychik;Tomasz P. Michalak

Robust Collective Classification against Structural Attacks

针对结构性攻击的鲁棒集体分类

• 发表时间: 2020
• 期刊: Conference on Uncertainty in Artificial Intelligence (UAI
• 作者: Zhou, Kai;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy