Social learning is a non-Bayesian framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although it is also possible to examine the case of heterogeneous models across the graph. One important special case is when heterogeneity is caused by the presence of malicious agents whose goal is to move the agents towards a wrong hypothesis. In this work, we propose an algorithm that allows to discover the true state of every individual agent based on the sequence of their beliefs. In so doing, the methodology is also able to locate malicious behavior.

翻译：社会学习是一种用于分布式假设检验的非贝叶斯框架，其目标在于学习环境的真实状态。传统上，假设智能体基于相同的真实状态接收观测值，但也可以考虑图中存在异质模型的情况。一个重要的特例是，异质性由恶意智能体引起，其目的是将其他智能体引向错误假设。在此项工作中，我们提出了一种算法，该算法能够基于每个智能体的信念序列发现其真实状态。通过这种方式，该方法还能定位恶意行为。