Imagine a simulated world where AI agents build, trade, and learn. How do different forms of governance shape their behaviors and incentives? This fascinating research explores precisely that, using two distinct AI approaches: Multi-Agent Reinforcement Learning (MARL) and Generative Agent-Based Modeling (GABM). In the MARL world, agents optimize their actions based on rewards, striving to maximize their virtual wealth in a simulated economy. Researchers tweaked the rules of this world, creating different governing systems – from libertarian to utilitarian – and observed how these systems influenced the agents' choices. Surprisingly, a system resembling our own democracies, where agents vote and the central planner counts those votes, encouraged the most house building compared to trading or skill-sharing. Meanwhile, in the GABM world, agents communicate using natural language, mimicking human interactions. Here, a 'game master' sets the rules and observes how the agents navigate the social landscape. Interestingly, when the game master prioritized equality, a more utilitarian system led to increased house building and skill development. However, when productivity was the focus, a libertarian approach seemed to stimulate all economic activities. While the results weren’t perfectly aligned between the two AI approaches, both demonstrated the power of simulation for understanding complex social dynamics. The agents, in both cases, were able to grasp the rules of their worlds and adapt accordingly. This research opens doors to more sophisticated simulations, allowing us to explore the intricate interplay of governance, incentives, and emergent behavior in AI-populated societies. What if we could model different economic systems, introduce complex social norms, or even study the evolution of artificial moral reasoning? The possibilities are as vast as the virtual worlds we create.