Non-Bayesian social learning enables multiple agents to conduct networked signal and information processing through observing environmental signals and information aggregating. Traditional non-Bayesian social learning models only consider single signals, limiting their applications in scenarios where multiple viewpoints of information are available. In this work, we exploit, in the information aggregation step, the independently learned results from observations taken from multiple viewpoints and propose a novel non-Bayesian social learning model for scenarios with multiview observations. We prove the convergence of the model under traditional assumptions and provide convergence conditions for the algorithm in the presence of misleading signals. Through theoretical analyses and numerical experiments, we validate the strong reliability and robustness of the proposed algorithm, showcasing its potential for real-world applications.

翻译：非贝叶斯社会学习使多个智能体能够通过观测环境信号和信息聚合来进行网络化信号与信息处理。传统的非贝叶斯社会学习模型仅考虑单一信号，限制了其在可获得多视角信息的场景中的应用。在本工作中，我们在信息聚合步骤中利用从多个视角观测得到的独立学习结果，提出了一种适用于多视角观测场景的新型非贝叶斯社会学习模型。我们在传统假设下证明了该模型的收敛性，并给出了算法在存在误导性信号时的收敛条件。通过理论分析和数值实验，我们验证了所提算法具有高度的可靠性与鲁棒性，展现了其在现实应用中的潜力。