Traditional social learning frameworks consider environments with a homogeneous state, where each agent receives observations conditioned on that true state of nature. In this work, we relax this assumption and study the distributed hypothesis testing problem in a heterogeneous environment, where each agent can receive observations conditioned on their own personalized state of nature (or truth). We particularly focus on community structured networks, where each community admits their own true hypothesis. This scenario is common in various contexts, such as when sensors are spatially distributed, or when individuals in a social network have differing views or opinions. We show that the adaptive social learning strategy is a preferred choice for nonstationary environments, and allows each cluster to discover its own truth.

翻译：传统社会学习框架考虑具有均匀状态的环境，其中每个智能体接收基于真实自然状态生成的观测。在本工作中，我们放宽这一假设，研究异构环境下的分布式假设检验问题，其中每个智能体可接收基于其自身个性化自然状态（或称真实状态）生成的观测。我们特别关注社区结构网络，其中每个社区拥有其自身的真实假设。这种场景在多种情境中普遍存在，例如当传感器空间分布时，或社交网络中的个体持有不同观点或意见时。我们证明自适应社会学习策略是非平稳环境下的首选方案，并能使每个聚类发现其自身的真实状态。