Learning and coordinating in a multilayer network

TL;DR

This paper presents a multilayer network model for social coordination where agents learn and make decisions based on social and strategic motivations, revealing how local interactions influence full coordination and strategy efficiency.

Contribution

It introduces a novel two-layer network model combining social learning and strategic decision-making, highlighting the impact of local connectivity on coordination outcomes.

Findings

Skepticism and local connectivity promote full coordination.

Polarized layers occur only with all-to-all interactions.

Local interactions enable coordination on Pareto-dominant strategies.

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 payoff , 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.