Learning and coordinating in a multilayer network

Abstract

We introduce a two layer network model for social coordination incorporating two relevant ingredients: a)
different networks of interaction to learn and to obtain a pay-off, and b) decision making processes based
both on social and strategic motivations. Two populations of agents are distributed in two layers with
intralayer learning processes and playing interlayer a coordination game. We find that the skepticism about
the wisdom of crowd and the local connectivity are the driving forces to accomplish full coordination of the
two populations, while polarized coordinated layers are only possible for all-to-all interactions. Local
interactions also allow for full coordination in the socially efficient Pareto-dominant strategy in spite of
being the riskier one.