192.099 Algorithmic Social Choice
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to:

  • explain and identify basic concepts, structures, and problems from collective decision making,
  • describe and design efficient algorithms, and analyze properties (e.g., computational complexity, existence or stability of solutions, characterizations) of problems arising in the context of collective decision making and related fields.


Subject of course

The course addresses problems at the intersection of economics, social choice theory, and computer science. The focus is on processes of algorithmic decision making, such as voting rules or fair division. We discuss fundamental concepts from collective decision making and related topics and investigate algorithmic and computational aspects.

Specific topics include:

  • aggregating preferences (rank aggregation) and voting,
  • preference domain restrictions,
  • matchings under preferences,
  • algorithmic mechanism design,
  • cake cutting protocols,
  • fair allocation of recourses, and
  • judgment aggregation.


Teaching methods

The course will consist of lectures and exercises. The students will receive an exercise sheet 1-2 weeks before each exercise and are expected to submit their solutions in advance and also to be able to present the solutions on the whiteboard. Exercise sheets will be available for download.

Mode of examination

Written and oral

Additional information

ECTS Breakdown

  • Lectures + exercises: 25 h
  • Preparation and follow-up: 26 h
  • Exam preparation: 23 h
  • Exam: 1 h


  • Sum:  75 h
  • ECTS: 3


  • U. Endriss, ed: Trends in Computational Social Choice. AI Access, 2017
  • F. Brandt, V. Conitzer, U. Endriss, J. Lang, and A. D. Procaccia, ed.: Handbook of Computational Social Choice. Cambridge University Press, 2015.
  • J. Rothe, ed.: Economics and Computation. An Introduction to Algorithmic Game Theory, Computational Social Choice, and Fair Division. Springer, 2015
  • Y. Shoham, K. Leyton-Brown: Multiagent Systems. Cambridge University Press, 2009.



Course dates

Wed14:00 - 16:0004.03.2020 - 11.03.2020FAV Hörsaal 2 Algorithmic Social Choice
Algorithmic Social Choice - Single appointments
Wed04.03.202014:00 - 16:00FAV Hörsaal 2 Algorithmic Social Choice
Wed11.03.202014:00 - 16:00FAV Hörsaal 2 Algorithmic Social Choice

Examination modalities

The final mark of each attendee depends on the final exam (60%) and his/her performance at the whiteboard exercises (40%).

Course registration

Begin End Deregistration end
03.03.2020 00:00 10.04.2020 23:59 10.04.2020 23:59



No lecture notes are available.

Previous knowledge

Basic knowledge of algorithmic design, e.g.

  1. Algorithmen und Datenstrukturen,
  2. Algorithmics (Master-VU),
  3. Komplexitätstheorie, etc.