Dynamic Global Games of Regime Change: Learning, Multiplicity, and the Timing of Attacks
扩展了静态全局博弈模型，允许代理人多期行动并随时间学习基本面，发现静态中保证唯一性的条件在动态中可能导致多重均衡，且基本面可预测最终结果但无法预测攻击时机。
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