New memory-one strategies of the Iterated Prisoner’s Dilemma: a new framework to programmed human-AI interaction

Abstract With the rapid and continuing development of AI, the study of human-AI interaction is increasingly relevant. In this sense, we propose a reference framework to explore a model development in the context of social science to try to extract valuable information to the AI context. The model we choose was the Prisoner Dilemma using the Markov chain approach to study the evolution of memory-one strategies used in the Prisoner’s Dilemma in different agent-based simulation contexts using genetic algorithms programmed on the NetLogo environment. We developed the Multiplayer Prisoner’s Dilemma simulation from deterministic and probabilistic conditions, manipulating not only the probability of communication errors (noise) but also the probability of finding again the same agent. Our results suggest that the best strategies depend on the context of the game, following the rule: the lower the probability of finding the same agent again, the greater the chance of defect. Therefore, in environments with a low probability of interaction, the best strategies were the ‘Always Defect’ ones. But as the number of interactions increases, a different strategy emerges that is able to win Always Defect strategies, such as the Spiteful/grim. In addition, our results also highlight strategies that emerge in situations in which Spiteful/grim and Always Defect were banned. These are memory-one strategies with better performance than both TFT and PAVLOV under all conditions showing behaviors that are particularly deceiving but successful. The previously memory-one strategies for the Prisoner Dilemma represent a set of extensively tested strategies in contexts with different probability of encountering each other again and provide a framework for programming algorithms that interact with humans in PD-like trusted contexts.