Network common learning is introduced, a network analogue of common learning, and it is shown 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.
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.
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