2022/01/26 by Yunus İnan, Mert Kayaalp, Inan, Yunus +5
Computer Science · Decision Sciences · #Auction Theory and Applications #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Electrical engineering #Game Theory and Applications #Multiagent Systems (cs.MA) #Signal Processing (eess.SP) #Social and Information Networks (cs.SI) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.10957
openalex publication_date 2022/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic convergence rate remains the same as the standard algorithms which use more communication resources. We also derive large deviation estimates of the log-belief ratios for a special case where each agent replaces its belief with that of the chosen neighbor.