Human–machine cooperation in social dilemma games: How human strategies shape machine learning and collective behavior

With the widespread application of artificial intelligence, human–machine interaction has become an essential component of social systems. This study investigates human–machine cooperation from an evolutionary game perspective by constructing a mixed spatial prisoner's dilemma environment that integrates reinforcement learning–based machine strategies and traditional reactive human strategies. The results show that machines interacting with tolerant human strategies tend to converge toward stable cooperative patterns and, under certain conditions, significantly enhance group cooperation. The effect of machine proportion is context-dependent: in low-temptation settings, machines strengthen cooperative stability, whereas in high-temptation environments, cooperation relies more on human strategies. Furthermore, the analysis of average Q-values reveals that machine learning not only reproduces conditional cooperation logic but is also deeply shaped by human strategic patterns. These findings highlight the critical role of humans in shaping machine learning and cooperative tendencies, offering new theoretical insights into the evolution of human–machine cooperation and methodological implications for applications such as intelligent manufacturing and autonomous driving.