Local Non-Bayesian Social Learning With Stubborn Agents
Local Non-Bayesian Social Learning With Stubborn Agents
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DOI：
10.1109/tcns.2022.3154679
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发表时间：
2022-09
影响因子：
4.2
通讯作者：
Daniel Vial;V. Subramanian
中科院分区：
文献类型：
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作者：
Daniel Vial;V. Subramanian
在本文中，我们研究了一个社会学习模型，在这个模型中，智能体使用私有信号和其他智能体的信念以非贝叶斯方式迭代地更新他们对世界真实状态的信念。有些代理人很固执，这意味着他们试图让其他人相信一个错误的真实状态（模仿假新闻）。我们表明，当智能体在短时间尺度上学习真实状态时，它们会“忘记”它，并相信错误状态在较长时间尺度上是真实的。利用这些结果，我们设计了播种顽固代理以破坏学习的策略，该策略优于直觉启发式，并对社会学习中的漏洞提供了新的见解。
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.