Memory depth of finite state machine strategies for the iterated prisoner's dilemma
arXiv:1912.04493
Abstract
We develop an efficient algorithm to determine the memory-depth of finite state machines and apply the algorithm to a collection of iterated prisoner's dilemma strategies. The calculation agrees with the memory-depth of other representations of common strategies such as Tit-For-Tat, Tit-For-2-Tats, etc. which are typically represented by lookup tables. Our algorithm allows the complexity of finite state machine based strategies to be characterized on the same footing as memory-n strategies.
References in corpus (3)
- Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma
- Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma
- The Art of War: Beyond Memory-one Strategies in Population Games