A meta analysis of tournaments and an evaluation of performance in the Iterated Prisoner's Dilemma
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The Iterated Prisoner's Dilemma has been used for decades as a model of behavioural interactions. From the celebrated performance of Tit for Tat, to the introduction of the zero-determinant strategies, to the use of sophisticated structures such as neural networks, the literature has been exploring the performance of strategies in the game for years. The results of the literature, however, have been relying on the performance of specific strategies in a finite number of tournaments. This manuscript evaluates 195 strategies' effectiveness in more than 40000 tournaments. The top ranked strategies are presented, and moreover, the impact of features on their success are analysed using machine learning techniques. The analysis determines that the cooperation ratio of a strategy in a given tournament compared to the mean and median cooperator is the most important feature. The conclusions are distinct for different types of tournaments. For instance a strategy with a theory of mind would aim to be the mean/median cooperator in standard tournaments, whereas in tournaments with probabilistic ending it would aim to cooperate 10
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