Serious Games: Human-AI Interaction, Evolution and Coevolution

Serious games have transcended entertainment to become analytical tools that model the interaction between humans and artificial intelligence. Inspired by evolutionary game theory —such as the iterated prisoner's dilemma, hawk-dove, or the war of attrition— these frameworks allow us to anticipate how cooperative and competitive strategies can shape the coevolution of both entities. Far from being just an academic exercise, this approach has practical applications in developing intelligent systems that dynamically adapt to human behavior. In this context, companies like Q2BSTUDIO apply principles of artificial intelligence and AI agents to design solutions that evolve alongside business needs, integrating predictive models and behavioral analysis in controlled environments. Evolutionary game theory suggests that, in repetitive scenarios, cooperation emerges as an optimal strategy if reciprocity mechanisms exist; this translates into the design of custom applications that foster trust between users and automated systems. On the other hand, competition for limited resources —such as attention or data— drives the specialization of agents, a phenomenon that business intelligence tools like Power BI can monitor to identify behavioral patterns. Cybersecurity also plays a crucial role: by modeling interactions with the war of attrition, companies can predict and mitigate threats through adaptive defense strategies. AWS and Azure cloud services offer the scalable infrastructure needed to simulate these coevolutionary scenarios on a large scale. Ultimately, understanding serious games between humans and AI is not only relevant for research, but also for organizations to implement AI for businesses that not only respond, but anticipate and adapt to the joint evolution of their users.