Please find further details in the eCampus course.

The exams will be oral, about 25-30 minutes long, 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 second period will take place on March 14, 15 and 16. Please contact Alexander Braun by March 3 to be assigned a time slot. Please also state your first and second most preferred day for the exam. You will receive a confirmation of the day of your exam immediately within a day or two, the exact time will only be announced a few days before the exam.

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.

• Tutorials, October 14
• Tutorials, October 21
• Tutorials, October 28
• Tutorials, November 4
• Tutorials, November 11
• Tutorials, November 18
• Tutorials, November 25
• Tutorials, December 2
• Tutorials, December 9
• Tutorials, December 16
• Tutorials, December 23
• Tutorials, January 13
• Tutorials, January 20
• Tutorials, January 27

• Problem Set 1, due October 20
• Problem Set 2, due October 27
• Problem Set 3, due November 3
• Problem Set 4, due November 10
• Problem Set 5, due November 17
• Problem Set 6, due November 24
• Problem Set 7, due December 1
• Problem Set 8, due December 8
• Problem Set 9, due December 15
• Problem Set 10, due December 22
• Problem Set 11, due January 12
• Problem Set 12, due January 19
• Problem Set 13, due January 26
• Problem Set 14, due February 2

• 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