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.

arXiv comment16 pages, 4 figures

References in corpus (5)

• The Evolution of Extortion in Iterated Prisoner's Dilemma Games
• From extortion to generosity, the evolution of zero-determinant strategies in the prisoner's dilemma
• Evolutionary instability of Zero Determinant strategies demonstrates that winning isn't everything
• The collapse of cooperation in evolving games
• Evolutionary Extortion and Mischief: Zero Determinant strategies in iterated 2x2 games

Cited by in corpus (6)

• The collapse of cooperation in evolving games
• Evolutionary consequences of behavioral diversity
• Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma
• An open reproducible framework for the study of the iterated prisoner's dilemma
• Single Equalizer Strategy with no Information Transfer for Conflict Escalation
• Memory depth of finite state machine strategies for the iterated prisoner's dilemma