Probabilistic memory-one strategies to dominate the iterated prisoner’s dilemma over networks

The Iterated Prisoner’s Dilemma (IPD) has been a classical game theoretical scenario used to model behaviour interactions among agents. From the famous Axelrod’s tournament, and successful results obtained by Tit for Tat strategy, introduction of zero-determinant strategies in last decade, theory community exploring performance multiple years. This article grounds on such previous work, studying probabilistic memory-one (PMO) using evolutionary theory, analyse criteria find most set networked topologies. are nearly deterministic discrete PMO scenarios. However, become much more complex when moving continuous ones, there is no optimal strategy given scenario. Finally, this describes how, machine learning techniques; cluster agents, playing synchronously adaptively, able dominate rest population.