Social Learning under Randomized Collaborations
Yunus İnan, Mert Kayaalp, Emre Telatar, Ali H. Sayed
Abstract
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.
Topics & Concepts
Convergence (economics)Computer scienceScheme (mathematics)InstantRate of convergenceArtificial intelligenceMachine learningTheoretical computer scienceMathematicsEconomicsPhysicsEconomic growthQuantum mechanicsComputer networkMathematical analysisChannel (broadcasting)Distributed Sensor Networks and Detection AlgorithmsGame Theory and ApplicationsAdvanced Bandit Algorithms Research