Engineering design of strategies for winning iterated prisoner's dilemma competitions

Document Type

Journal Article


Faculty of Computing, Health and Science


School of Computer and Security Science / Artificial Intelligence and Optimisation Research Centre




Li, J., Hingston, P. F., & Kendall, G. (2011). Engineering design of strategies for winning iterated prisoner's dilemma competitions. IEEE Transactions on Computational Intelligence and AI in Games, 3(4), 348-360. Available here


In this paper, we investigate winning strategies for round-robin iterated Prisoner's Dilemma (IPD) competitions and evolutionary IPD competitions. Since the outcome of a single competition depends on the composition of the population of participants, we propose a statistical evaluation methodology that takes into account outcomes across varying compositions. We run several series of competitions in which the strategies of the participants are randomly chosen from a set of representative strategies. Statistics are gathered to evaluate the performance of each strategy. With this approach, the conditions for some well-known strategies to win a round-robin IPD competition are analyzed. We show that a strategy that uses simple rule-based identification mechanisms to explore and exploit the opponent outperforms well-known strategies such as tit-for-tat (TFT) in most round-robin competitions. Group strategies have an advantage over nongroup strategies in evolutionary IPD competitions. Group strategies adopt different strategies in interacting with kin members and nonkin members. A simple group strategy, Clique, which cooperates only with kin members, performs well in competing against well-known IPD strategies. We introduce several group strategies developed by combining Clique with winning strategies from round-robin competitions and evaluate their performance by adapting three parameters: sole survivor rate, extinction rate, and survival time. Simulation results show that these group strategies outperform well-known IPD strategies in evolutionary IPD competitions.




Link to publisher version (DOI)