Abstract
The Iterated Prisoner’s Dilemma (IPD) game is a one of the most popular subjects of study in game theory. Numerous experiments have investigated many properties of this game over the last decades. However, topics related to the simulation scale did not always play a significant role in such experimental work. The main contribution of this paper is the optimization of IPD strategies performed in a distributed actor-based computing and simulation environment. Besides showing the scalability and robustness of the framework, we also dive into details of some key simulations, analyzing the most successful strategies obtained.
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Starzec, G., Starzec, M., Byrski, A., Kisiel-Dorohinicki, M., Burguillo, J. C., & Lenaerts, T. (2019). Towards Large-Scale Optimization of Iterated Prisoner Dilemma Strategies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11370 LNCS, pp. 167–183). Springer Verlag. https://doi.org/10.1007/978-3-662-58611-2_4
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