Algorithmic Game Theory for OR

Course Info

• Instructor: Christian Kroer
• Time: Mondays & Wednesdays 10:10-11:25pm
• Location: 337 Mudd
• Office hours: Wednesday after class

Course Summary

This is a graduate-level course on Algorithmic Game Theory and Mechanism Design. We will cover some of the theoretical foundations of game theory and mechanism design, and cover a number of the most important AGT and MD algorithms that are used in practice. We will take a very optimization-centric view towards AGT, and indeed we will see that an optimization lens is a very fruitful perspective on AGT, both in theory and practice.

We will have about 24 lectures total.

We will describe several practical applications, including how to:

• Fairly allocate course seats to students, food to food banks, etc
• Protect wildlife or airports
• Conduct auctions for Internet ads or electricity

Course Structure

The course will be lecture-based, with Christian Kroer giving the lectures. We will also have about seven guest lectures by Jakub Cerny. At the end of the course there will be a few lectures of project presentations by students.

Readings will consist of excerpts from several textbooks.

Students will complete a project, which may be done individually or in groups of 2-3 students.

Grading will be as follows:

• 65% final project write-up
• 20% Final project presentation
• 10% Participation
• 5% Project proposal

Prerequisites

This is intended to be a PhD-level course for students in Operations Research and adjacent areas such as computer science, economics, and statistics. The most important prerequisite is mathematical maturity. Students should have a strong foundation in optimization (including convex optimization and duality) and applied probability. Familiarity with basic concepts from algorithm design and analysis will be helpful. Students are expected to be comfortable with linear algebra, calculus, and some basic real analysis.

Students from outside OR may have less optimization background than will be assumed for the course. It should be possible for a mathematically-mature PhD student to pick up the necessary background as we go along.

Advanced undergrads and MS students may take the class pending discussion with me about having sufficient background (I will require you to have taken some PhD-level mathematics or optimization) and motivation for taking the course.

Textbooks

There is no single textbook that will cover everything in the course. We will use my textbook for a number of topics. Below I also list a few other books that we are likely to draw from.

• Games, Markets, and Online Learning (CK). Christian Kroer (forthcoming in print March 2026).
• Algorithmic Game Theory (AGT) by Nisan, Roughgarden, Tardos, and Vazirani.
• Twenty Lectures on Algorithmic Game Theory (TLAGT) by Tim Roughgarden (the individual notes can be found on Tim’s website under the course “Algorithmic Game Theory”).
• A Modern Introduction to Online Learning (MIOL) by Orabona. Free.

Schedule

Related Courses

Below is a list of related courses at other schools.