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Local Global Games and Network Common Learning

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  • Olga Rospuskova
  • Omer Tamuz
  • Jake Zhang

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.

Suggested Citation

  • Olga Rospuskova & Omer Tamuz & Jake Zhang, 2026. "Local Global Games and Network Common Learning," Papers 2607.28821, arXiv.org.
    • Handle: RePEc:arx:papers:2607.28821
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    File URL: https://arxiv.org/pdf/2607.28821
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