Learning and evolution in games and oligopoly models

Dynamic models of adjustment, as well as static models of equilibrium, are important to understand economic reality. This thesis considers such dynamic models applied to economic games. The models can broadly be divided into two categories: learning and evolution. This thesis analyzes reinforcement learning and imitation dynamic on the learning side and the indirect evolution approach on the evolution side. It demonstrates the relation between the concept of Nash equilibrium and the long run outcome of the namic processes. The applications of the dynamic models to economic games include, among others, Cournot oligopoly and merger games.