  Following the Bayesian communication learning paradigm, we propose a finite
population learning concept to capture the level of information aggregation in
any given network, where agents are allowed to communicate with neighbors
repeatedly before making a single decision. This concept helps determine the
occurrence of effective information aggregation in a finite network and reveals
explicit interplays among parameters. It also enables meaningful comparative
statics regarding the effectiveness of information aggregation in networks.
Moreover, it offers a solid foundation to address, with a new perfect learning
concept, long run dynamics of learning behavior and the associated learning
rates as population diverges. Our conditions for the occurrence of finite
population learning and perfect learning in communication networks are very
tractable and transparent.
