Local Global Games and Network Common Learning

2026-07-30

AI Summary

This study investigates how agents in social networks achieve global coordination through local interactions and private signals, with a focus on how network structure shapes higher-order beliefs and equilibrium behavior. The authors develop a local-global game framework in which each agent makes decisions based on signals from neighbors within graph distance \( r \), and introduce the novel concept of “networked common learning” to characterize coordination efficiency. Integrating global game theory, social network analysis, and Bayesian inference, they employ graph-theoretic methods to analyze learning dynamics across different topologies. Their results show that structures such as two-dimensional grids support networked common learning and enable efficient coordination, whereas architectures with information bottlenecks—like linear chains—converge only to risk-dominant equilibria.

Abstract

We study global games in which agents coordinate locally, with their social network neighbors, contingent on a favorable state. Before acting, agents learn the private signals of all agents within network distance $r$. As $r$ grows, every agent learns the state, but efficient coordination depends on higher-order beliefs, which are shaped by the geometry of the network. We introduce network common learning, a network analogue of common learning, and show that it is attained when neighboring agents' observations differ by many signals, as on the two-dimensional grid, but fails on networks with informational bottlenecks, such as the line, where only the safe action survives in equilibrium.

Problem

• global games
• network common learning
• social networks
• coordination
• higher-order beliefs

Innovation

• network common learning
• global games
• higher-order beliefs
• informational bottlenecks
• network geometry