Properties of winning Iterated Prisoner’s Dilemma strategies

Researchers have explored the performance of Iterated Prisoner’s Dilemma strategies for decades, from the celebrated performance of Tit for Tat to the introduction of the zero-determinant strategies and the use of sophisticated learning structures such as neural networks. Many new strategies have been introduced and tested in a variety of tournaments and population dynamics. Typical results in the literature, however, rely on performance against a small number of somewhat arbitrarily selected strategies, casting doubt on the generalizability of conclusions. In this work, we analyze a large collection of 195 strategies in thousands of computer tournaments, present the top performing strategies across multiple tournament types, and distill their salient features. The results show that there is not yet a single strategy that performs well in diverse Iterated Prisoner’s Dilemma scenarios, nevertheless there are several properties that heavily influence the best performing strategies. This refines the properties described by Axelrod in light of recent and more diverse opponent populations to: be nice, be provocable and generous, be a little envious, be clever, and adapt to the environment. More precisely, we find that strategies perform best when their probability of cooperation matches the total tournament population’s aggregate cooperation probabilities. The features of high performing strategies help cast some light on why strategies such as Tit For Tat performed historically well in tournaments and why zero-determinant strategies typically do not fare well in tournament settings.Author summary: In 1980, political scientist Robert Axelrod ran one of the most famous computer tournaments of the Iterated Prisoner’s Dilemma (IPD). The winner? The now-famous strategy, Tit for Tat. Axelrod attributed its success to simple properties such as: do not be envious, avoid being the first to defect, and do not be overly clever. Yet the tournament design, using only a small, selected set of strategies, not including random noise, and having fixed game lengths, raises questions about the generalizability of these results. Many researchers have continued to make similar assumptions in their own IPD experiments, limiting the insights that can be applied to more complex, realistic settings.