  We propose an opinion dynamics model in which agents gradually increase their
own self-confidence while interacting with each other. The relations between
the newly proposed model and existing works of social learning, inertial
opinion dynamics, Bayesian inference, and stochastic multi-armed bandits are
demonstrated. We prove the convergence of the system with the existence of a
truth under fixed and periodically changing social networks, and obtain tight
convergence bounds related to the spectral gap of the graph Laplacian and the
maximum total degree centrality, respectively. In the case of randomly
generated social networks, an almost-sure convergence result is obtained. The
dynamics of the model with multiple truths or zero truth is also discussed.
