As AI agents become more capable, they face the same strategic challenges humans do. How should autonomous vehicles coordinate at intersections? How should trading algorithms interact? How should AI assistants negotiate on behalf of their users?

Game theory provides the framework. Multi-agent reinforcement learning incorporates strategic reasoning. Mechanism design ensures incentive compatibility in AI marketplaces. The Shapley value can determine how to attribute value in collaborative AI systems.

But there's a deeper question: how do we ensure AI agents cooperate rather than compete destructively? The lessons from 2,400 years of human strategic thinking - cooperation in repeated games, fair division, trust-building - apply directly.

In this topic, you'll explore how game theory informs AI system design, what cooperation protocols work for autonomous agents, and how the wisdom of the masters can guide the development of AI that creates value rather than destroys it. The synthesis is complete when ancient wisdom meets modern technology.