Evolutionary Drift and Equilibrium Selection
基于交互学习模型的微分方程，研究均衡选择问题。当纳什均衡在均衡路径上行为相同但路径外行为不同时，会形成连通的平稳状态分量。文章分析了扰动对分量稳定性的影响，并提供了漂移产生强稳定性平稳状态的充分条件，用于推导纳什均衡精炼文献中的比较静态预测。
Abstract: This paper develops an approach to equilibrium selection in game theory based on studying the equilibriating process through which equilibrium is achieved. The differential equations derived from models of interactive learning typically have stationary states that are not isolated. Instead, Nash equilibria that specify the same behavior on the equilibrium path, but different out-of-equilibrium behavior, appear in connected components of stationary states. The stability properties of these components often depend critically on the perturbations to which the system is subjected. We argue that it is then important to incorporate such drift into the model. A sufficient condition is provided for drift to create stationary states with strong stability properties near a component of equilibria. This result is used to derive comparative static predictions concerning common questions raised in the literature on refinements of Nash equlibrium.;