Inertial Coordination Games

TL;DR

This paper studies inertial coordination games where the evolving state depends on players' private learning and past actions, revealing how learning speed influences long-term equilibrium outcomes and the emergence of self-fulfilling spirals.

Contribution

It introduces a dynamic framework for coordination games with endogenous state changes driven by learning and past play, extending static global game results.

Findings

Slow learning leads to risk-dominant actions in the long run.

Fast learning can cause shocks to propagate, creating self-fulfilling spirals.

The model generalizes static global game results to dynamic settings.

Abstract

We analyze inertial coordination games: dynamic coordination games with an endogenously changing state that depends on (i) a persistent fundamental players privately learn about over time; and (ii) past play. The speed of learning determines long-run equilibrium dynamics: the risk-dominant action is played in the limit if and only if learning is slow such that posterior precisions grow sub-quadratically. This generalizes results from static global games and endows them with a learning foundation. Conversely, when learning is fast such that posterior precisions grow super-quadratically, shocks can propagate and generate self-fulfilling spirals.