Dynamic Global Games of Regime Change: Learning, Multiplicity and Timing of Attacks

Global games of regime change–coordination games of incomplete information in which a status quo is abandoned once a sufficiently large fraction of agents attacks it–have been used to study crises phenomena such as currency attacks, bank runs, debt crises, and political change. We extend the static benchmark examined in the literature by allowing agents to take actions in many periods and to learn about the underlying fundamentals over time. We first provide a simple recursive algorithm for the characterization of monotone equilibria. We then show how the interaction of the knowledge that the regime survived past attacks with the arrival of information over time, or with changes in fundamentals, leads to interesting equilibrium properties. First, multiplicity may obtain under the same conditions on exogenous information that guarantee uniqueness in the static benchmark. Second, fundamentals may predict the eventual regime outcome but not the timing or the number of attacks. Finally, equilibrium dynamics can alternate between phases of tranquillity–where no attack is possible–and phases of distress–where a large attack can occur–even without changes in fundamentals.