Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage...

After successful completion of the course, students will be 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.

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.

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.

ECTS Breakdown

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

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• Sum:  75 h
• ECTS: 3

Literature

• 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.

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

 Grundkenntnisse in Entwurf und Analyse von Algorithmen, z.B. aus

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