2024/03/19 by Valentina Shumovskaia, Shumovskaia, Valentina, Mert Kayaalp +3
Computer Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Multiagent Systems (cs.MA) #Network Security and Intrusion Detection #Signal Processing (eess.SP) #Social and Information Networks (cs.SI) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.12619
openalex publication_date 2024/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Non-Bayesian social learning is a 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 toward a wrong hypothesis. In this work, we propose an algorithm that allows discovering 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.