Local Non-Bayes an Social Learning With Stubborn Agents

【Author】 Vial, Daniel; Subramanian, Vijay

【Source】IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS

【影响因子】4.347

【Abstract】In this article, we study a social learning model in which agents iteratively update their beliefs about the true state of the world using private signals and the beliefs of other agents in a non-Bayesian manner. Some agents are stubborn, meaning they attempt to convince others of an erroneous true state (modeling fake news). We show that while agents learn the true state on short timescales, they "forget" it and believe the erroneous state to be true on longer timescales. Using these results, we devise strategies for seeding stubborn agents so as to disrupt learning, which outperforms intuitive heuristics and gives novel insights regarding vulnerabilities in social learning.

【Keywords】Complex networks; consensus algorithm; social network theory

【发表时间】2022 SEP

【收录时间】2022-10-02

【文献类型】理论模型

【主题类别】

区块链治理-市场治理-社会网络