MA-INF 1301: Algorithmic Game Theory

Please find further details in the eCampus course.


When Where Start Lecturer
Two lectures per week online, prerecorded April 8 Kesselheim

Recap and Reflect Session

When Where Start Lecturer
Wednesday, 12:15-13:45 CP1-HSZ / Hörsaal 3 April 10 Kesselheim


When Where Start Lecturer
Thursday, 10:15-11:45 Seminar room 2.050 April 11 Heuser
Thursday, 12:15-13:45 Seminar room 2.050 0.107 April 11 Heuser

Flipped Classroom

There will be no lectures in the lecture hall. Instead we will upload 2 prerecorded lecture videos every week, which you can watch in your own time at home. Additionally there will be a Recap and Reflect Session every Wednesday. During these sessions, we'll briefly summarize the key points covered in the week's lectures, offer a platform for any questions you may have, and facilitate discussions around simple exercises to reinforce your understanding of the new topics.


The exams will be oral and about 25-30 minutes long. There is a requirement for participating in the exams. Once during the semester, you need to present a solution for one of the homework exercises to your fellow students in the tutorial sessions. If you would like to present a solution in one of the tutorials, please send an email to Anna Heuser until Tuesday 10:00 pm including the task you would like to present in which of the tutorial sessions. Of course, sending the email earlier than Monday evening is also possible and recommended. After you have received the confirmation, we will schedule a short meeting (10-15min) for a quick pre-discussion on your solution.


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.

Problem Sets - Homework

Problem Sets - Tutorial

Further Reading

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