Computer AIs can now play Dota, Starcraft, and Go better than nearly any human being. But have you ever wondered what happens when dozens, hundreds, or millions of these intelligent agents face off each other? This is not a hypothetical question: many phenomena in the social world, from financial market action to international relations, are formed by the interaction of sophisticated entities. In this course, we will go through the basics of how computers make decisions and learn, building up to an introduction to reinforcement learning. We will discuss insights from game theory and behavioral economics to connect them to human decision-making. Finally, we will talk about how these tools can be used to study complex systems like networks and supply chains, where we see the emergence of collective behaviors like systemic risks.

Can we write a more efficient algorithm for fire propagation in Minecraft? What does it have in common with social movements, disease spread, and quantum complexity? Ideas in complexity theory manifest themselves in diverse, seemingly disconnected systems that come together to form a beautiful picture of how the universe functions. We’ll be giving a whirlwind tour of this exciting subject and connecting the dots between the disparate fields of computer algorithms, physics, and social science.