The Art of War: Beyond Memory-one Strategies in Population Games

Abstract

We define a new strategy for population games based on techniques from machine learning and statistical inference that is essentially uninvadable and can successfully invade (significantly more likely than a neutral mutant) essentially all known memory-one strategies for the prisoner's dilemma and other population games, including ALLC (always cooperate), ALLD (always defect), tit-for-tat (TFT), win-stay-lose-shift (WSLS), and zero determinant (ZD) strategies, including extortionate and generous strategies. We will refer to a player using this strategy as an "information player" and the specific implementation as $I{P}_0$. Such players use the history of play to identify opponent's strategies and respond accordingly, and naturally learn to cooperate with each other.

16 pages, 4 figures