Learning the Truth in Social Networks Using Multi-Armed Bandit
This paper explains how agents in a social network can learn Black Tea the arbitrary time-varying true state of the network.This is practical in social networks where information is released and updated without any coordination.Most existing literature for learning the true state using the non-Bayesian learning approach, assumes that this true stat