Algorithmic Game Theory
Module IN2239
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Basic Information
IN2239 is a semester module in English language at Bachelor’s level and Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 60 h | 5 CP |
Content, Learning Outcome and Preconditions
Content
Algorithmic game theory is a young research area at the intersection of theoretical computer science, mathematics, and economics that deals with the optimal strategic behavior in interactive situations. In this course, particular attention will be paid to algorithmic aspects of game-theoretic solution concepts such as Nash equilibrium and the design of economic mechanisms.
Learning Outcome
Upon completion of the module students are able to
- understand the foundations of algorithmic game theory,
- analyze different representations of n-player games,
- compute and discuss various solutions concepts,
- reason about the computational complexity of these solution concepts, and
- analyze and sketch simple algorithms to find solutions for game-theoretic problems.
- understand the foundations of algorithmic game theory,
- analyze different representations of n-player games,
- compute and discuss various solutions concepts,
- reason about the computational complexity of these solution concepts, and
- analyze and sketch simple algorithms to find solutions for game-theoretic problems.
Preconditions
Modul IN0015 Discrete Structures (or equivalent)
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Algorithmic Game Theory (IN2239) | Brandt, F. |
Tue, 15:00–16:30, Interims I 101 |
eLearning |
| UE | 2 | Exercise for Algorithmic Game Theory (IN2239) | Brandt, F. | dates in groups |
Learning and Teaching Methods
The module conists of lectures and accompanying tutorials. The contents of the course will be primarily presented in the form of lectures. The students are encouraged to independently deal with problems that are provided in the form of exercise sheets. Solutions to these exercises will be discussed in the tutorials.
Media
Slides, whiteboard sketches
Literature
Noam Nisan, Tim Roughgarden, Eva Tardos, and Vijay Vazirani: Algorithmic Game Theory (Cambridge University Press, 2007)
Martin Osborne and Ariel Rubinstein: A Course in Game Theory (MIT Press, 1994)
Robert Aumann: Game Theory, in J. Eatwell, M. Milgate, and P. Newman: The New Palgrave, A Dictionary of Economics, Vol. 2 (MacMillan, 1987)
Yoav Shoham, Kevin Leyton-Brown: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations (Cambridge University Press, 2009)
Martin Osborne and Ariel Rubinstein: A Course in Game Theory (MIT Press, 1994)
Robert Aumann: Game Theory, in J. Eatwell, M. Milgate, and P. Newman: The New Palgrave, A Dictionary of Economics, Vol. 2 (MacMillan, 1987)
Yoav Shoham, Kevin Leyton-Brown: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations (Cambridge University Press, 2009)
Module Exam
Description of exams and course work
The exam takes the form of a 120 minutes written test. In the exam students should prove to be able to
- identify a given game-theoretic problem,
- establish connections to related questions discussed in the lectures, and
- find solutions to these problems within a given time limit.
Furthermore, based on APSO Section 6 Subsection 5(2), there are 10-20 voluntary online mid-term exercises. The grade for these exercises can be used to improve the grade of a passed exam, i.e., if you pass the exam and your midterm grade is better than your exam grade, then your final grade will be the 80:20 weighted average of your exam grade and your midterm grade.
- identify a given game-theoretic problem,
- establish connections to related questions discussed in the lectures, and
- find solutions to these problems within a given time limit.
Furthermore, based on APSO Section 6 Subsection 5(2), there are 10-20 voluntary online mid-term exercises. The grade for these exercises can be used to improve the grade of a passed exam, i.e., if you pass the exam and your midterm grade is better than your exam grade, then your final grade will be the 80:20 weighted average of your exam grade and your midterm grade.