MGTECON621

In our digital economy, it can be difficult to understand markets without understanding the algorithms that underlie them. Similarly, it can be difficult to design effective algorithms without taking into account the preferences and incentives of the humans they serve. Recognizing that, this course covers topics at the intersection of economics and computer science. The primary topic this year is the theory of recommender systems: how to help consumers find products that they value. We will explore these systems through the lens of mechanism design, econometrics, and bounded rationality. Secondary topics may include algorithmic mechanism design, preference elicitation, privacy, algorithmic collusion, and AI alignment. Students will be introduced to relevant tools from computational complexity and statistical learning theory. Prerequisites: PhD-level course in microeconomic theory.