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，旨在学习环境的真实状态。传统上，假设所有智能体基于相同的真实状态接收观测数据，尽管也可以研究图中存在异构模型的情况。一个重要的特殊情形是当异构性由恶意智能体引起时，这些智能体的目标是引导其他智能体走向错误的假设。本文提出一种算法，能够根据每个智能体的信念序列发现其真实状态。通过这种方法，该机制还能够定位恶意行为。