The exams will be oral and take place as a video conference via Zoom. You will need to show and identify yourself on camera. It does not matter whether it is a computer or a smartphone. No other hardware is required. If you would like to take the exam but not in this form as a video conference, please contact us.

The first period will take place from February 22 to 26. Please contact Alexander Braun by January 31 to be assigned a time slot. Please also state your preference which of these days you like best and whether your exam should be in the morning or in the afternoon. At the moment, we will only determine the day of your exam and whether it will take place in the morning or afternoon. The exact time will only be announced a few days before the exam. If you do not plan to take the exam in the first period, you can also be assigned a time slot for the second period already, which will start on March 15.

One more thing: If you have been assigned a time slot but then decide to not take the exam, please remember to cancel it. It is very important for us to know that you will not show up because otherwise we will be waiting for you. In this case, send an e-mail to Thomas Kesselheim or Alexander Braun. Even a last-minute cancellation is better than a no-show. Of course, make sure that you also follow the official procedures (if applicable).

Throughout the world of modern computer networks, there are environments in which participants act strategically. Just consider internet service providers, which strive to route packets as cheaply as possible. Another example are cloud-based services: End-users and service providers rent remote infrastructure for storage or computations, giving rise to huge markets. Last but not least, advertisers want to reach their audience as cheaply as possible. This is the foundation of the business models of the world’s largest companies.

In all these settings, algorithms either act as selfish agents or have to cope with such. This brings about novel questions that are out of the scope of traditional algorithmic theory. Algorithmic game theory, a research direction at the intersection of game theory and algorithm design, has emerged to provide answers. On the one hand, this means to take analytical point of view and to strive to explain the performance of a given system. On the other hand, one also takes engineering perspective, asking how to design systems so that they can cope with selfishly acting agents.

In this course, we will introduce you to the foundations of algorithmic game theory, including

• basic game theory,
• computability and hardness of equilibria,
• convergence of dynamics of selfish agents,
• (bounds on the) loss of performance due to selfish behavior,
• designing incentive-compatible auctions
• maximizing revenue, and
• designing mechanisms for stable and fair allocations without money.

You should bring a solid background in algorithms and calculus. No prior knowledge on game theory is required. Specialized knowledge about certain algorithms is not necessary.

Lecture Videos on YouTube

• Lecture 1
• Lecture 2
• Lecture 3
• Lecture 4
• Lecture 5
• Lecture 6
• Lecture 7
• Lecture 8
• Lecture 9
• Lecture 10
• Lecture 11
• Lecture 12
• Lecture 13
• Lecture 14
• Lecture 15
• Lecture 16
• Lecture 17
• Lecture 18
• Lecture 19
• Lecture 20
• Lecture 21
• Lecture 22
• Lecture 23
• Lecture 24
• Lecture 25

• Tutorials, November 5
• Tutorials, November 12
• Tutorials, November 19
• Tutorials, November 26
• Tutorials, December 3
• Tutorials, December 10
• Tutorials, December 17
• Tutorials, January 7
• Tutorials, January 14
• Tutorials, January 21
• Tutorials, January 28
• Tutorials, February 4

• Problem Set 1, due November 11
• Problem Set 2, due November 18
• Problem Set 3, due November 25
• Problem Set 4, due December 2
• Problem Set 5, due December 9
• Problem Set 6, due December 16
• Problem Set 7, due January 6
• Problem Set 8, due January 13
• Problem Set 9, due January 20
• Problem Set 10, due January 27
• Problem Set 11, due February 3
• Problem Set 12, due February 10

• Algorithmic Game Theory, edited by Nisan, Roughgarden, Tardos, and Vazirani, available online under “Resources”
• Twenty Lectures on Algorithmic Game Theory, by Tim Roughgarden; also see his original lecture notes that are available online
• Game Theory, Alive! by Karlin and Peres, available online
• Multiagent Systems by Shoham and Leyton-Brown, available online
• Lecture notes on Algorithmic Mechanism Design by Jason Hartline, available online