Game Theory, Data Science and AI

The course explores applied topics at the intersection of game theory, data science, and artificial intelligence. The first part of the course focuses on computational approaches to solving complex games (such as Poker), with applications in developing successful algorithmic agents for playing these games. Lectures provide the foundations of the methods underlying these computational game theory approaches (rooted in the theory of learning in games and online learning theory) and proofs of their properties, while assignments involve implementing simple variants. The second part of the course examines the interplay between data science and mechanism design. Topics include optimizing auctions and mechanisms from data, with applications to online auction markets. The course also covers methodologies for learning structural parameters in games and econometrics in games, and how these techniques are used to analyze data from strategic interactions, such as auction data. The third part of the course explores topics related to A/B testing in markets with strategic interactions, with applications to digital matching platforms such as Airbnb. Prerequisites include mathematical maturity in probability, statistics, optimization, linear algebra, and calculus.