title: 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.
authors.affiliation: Department of Quantitative Economics, Universidad Complutense de Madrid, 28223 Madrid, Spain.
authors.forename: Haydée
authors.initials: H
authors.lastname: Lugo
authors.affiliation: IFISC (CSIC-UIB), Campus Universitat de les Illes Balears, 07122 Palma de Mallorca, Spain.
authors.forename: Maxi
authors.initials: M
authors.lastname: San Miguel
mesh_terms: D003198:Computer Simulation; D005716:Game Theory; D006801:Humans; D007858:Learning; D008962:Models, Theoretical; D012944:Social Support; D013997:Time Factors