Learning and Efficiency in Games with Dynamic Population (original) (raw)

We study the quality of outcomes in repeated games when the population of players is dynamically changing and participants use learning algorithms to adapt to the changing environment. Game theory classically considers Nash equilibria of one-shot games, while in practice many games are played repeatedly, and in such games players often use algorithmic tools to learn to play in the given environment. Learning in repeated games has only been studied when the population playing the game is stable over time.