通过影响力操纵选举:概率模型

IF 2 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Liangde Tao, Lin Chen, Lei Xu, Shouhuai Xu, Zhimin Gao, Weidong Shi
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引用次数: 0

摘要

我们认为这是对基本选举操纵问题的自然概括,即贿赂者可以通过影响力改变选民的意见或偏好。这是由现代政治竞选推动的,候选人试图通过电视、报纸、互联网等媒体说服选民。与经典的贿赂问题相比,我们并不假设贿赂者会直接用金钱换取个人选民的选票,而是假设贿赂者有一套潜在的竞选策略。每一种竞选策略都代表着对选民施加影响的某种方式。竞选策略有一定的成本,可以影响一部分选民。如果选民属于竞选策略的受众,那么他/她就会受到影响。如果选民受到更多竞选策略的影响,他/她将更有可能改变自己的观点/偏好。我们通过一个独立的激活模型对此进行建模,该模型在社会科学研究和计算复杂性研究中被广泛采用。在本文中,我们通过展示NP硬度的结果,并建立一个近似最优的固定参数可处理算法,给出了一个任意接近最优解的解,给出了完整的刻画。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Electoral manipulation via influence: probabilistic model

Electoral manipulation via influence: probabilistic model

We consider a natural generalization of the fundamental electoral manipulation problem, where a briber can change the opinion or preference of voters through influence. This is motivated by modern political campaigns where candidates try to convince voters through media such as TV, newspaper, Internet. Compared with the classical bribery problem, we do not assume the briber will directly exchange money for votes from individual voters, but rather assume that the briber has a set of potential campaign strategies. Each campaign strategy represents some way of casting influence on voters. A campaign strategy has some cost and can influence a subset of voters. If a voter belongs to the audience of a campaign strategy, then he/she will be influenced. A voter will be more likely to change his/her opinion/preference if he/she has received influence from a larger number of campaign strategies. We model this through an independent activation model which is widely adopted in social science research and study the computational complexity. In this paper, we give a full characterization by showing NP-hardness results and establishing a near-optimal fixed-parameter tractable algorithm that gives a solution arbitrarily close to the optimal solution.

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来源期刊
Autonomous Agents and Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems 工程技术-计算机:人工智能
CiteScore
6.00
自引率
5.30%
发文量
48
审稿时长
>12 weeks
期刊介绍: This is the official journal of the International Foundation for Autonomous Agents and Multi-Agent Systems. It provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Coverage in Autonomous Agents and Multi-Agent Systems includes, but is not limited to: Agent decision-making architectures and their evaluation, including: cognitive models; knowledge representation; logics for agency; ontological reasoning; planning (single and multi-agent); reasoning (single and multi-agent) Cooperation and teamwork, including: distributed problem solving; human-robot/agent interaction; multi-user/multi-virtual-agent interaction; coalition formation; coordination Agent communication languages, including: their semantics, pragmatics, and implementation; agent communication protocols and conversations; agent commitments; speech act theory Ontologies for agent systems, agents and the semantic web, agents and semantic web services, Grid-based systems, and service-oriented computing Agent societies and societal issues, including: artificial social systems; environments, organizations and institutions; ethical and legal issues; privacy, safety and security; trust, reliability and reputation Agent-based system development, including: agent development techniques, tools and environments; agent programming languages; agent specification or validation languages Agent-based simulation, including: emergent behavior; participatory simulation; simulation techniques, tools and environments; social simulation Agreement technologies, including: argumentation; collective decision making; judgment aggregation and belief merging; negotiation; norms Economic paradigms, including: auction and mechanism design; bargaining and negotiation; economically-motivated agents; game theory (cooperative and non-cooperative); social choice and voting Learning agents, including: computational architectures for learning agents; evolution, adaptation; multi-agent learning. Robotic agents, including: integrated perception, cognition, and action; cognitive robotics; robot planning (including action and motion planning); multi-robot systems. Virtual agents, including: agents in games and virtual environments; companion and coaching agents; modeling personality, emotions; multimodal interaction; verbal and non-verbal expressiveness Significant, novel applications of agent technology Comprehensive reviews and authoritative tutorials of research and practice in agent systems Comprehensive and authoritative reviews of books dealing with agents and multi-agent systems.
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