MA-INF 1301: Algorithmic Game Theory

Please find further details in the eCampus course.


When Where Start Lecturer
Monday, 10:15-11:45 CP1-HSZ / Hörsaal 3 April 3 Kesselheim
Wednesday, 12:15-13:45 CP1-HSZ / Hörsaal 3 April 5 Kesselheim


When Where Start Lecturer
Thursday, 10:15-11:45 Seminar room 2.050 April 6 Heuser
Thursday, 12:15-13:45 Seminar room 2.050 April 6 Heuser

Next Weeks

Wed, May 17 Lecture 12
Thu, May 18 No Tutorials (Holiday)
Mon, May 22 Lecture 13
Wed, May 24 No Lecture (Dies Academicus)
May 29, 31, June 1 No Lectures or Tutorials
Mon, June 5 No Lecture
Wed, June 7 Tutorial instead of Lecture in Lecture Hall
Thu, June 8 No Tutorials (Holiday)
Mon, June 12 Regular schedule resumes with Lecture 17

Videos of Lectures 14, 15, and 16 will be published online.


The exams will be oral and the first exam period will take place from July 31 to August 04. The second exam period will take place from August 28 to September 1.


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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