  A network of agents attempt to learn some unknown state of the world drawn by
nature from a finite set. Agents observe private signals conditioned on the
true state, and form beliefs about the unknown state accordingly. Each agent
may face an identification problem in the sense that she cannot distinguish the
truth in isolation. However, by communicating with each other, agents are able
to benefit from side observations to learn the truth collectively. Unlike many
distributed algorithms which rely on all-time communication protocols, we
propose an efficient method by switching between Bayesian and non-Bayesian
regimes. In this model, agents exchange information only when their private
signals are not informative enough; thence, by switching between the two
regimes, agents efficiently learn the truth using only a few rounds of
communications. The proposed algorithm preserves learnability while incurring a
lower communication cost. We also verify our theoretical findings by simulation
examples.
