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.

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 resources, 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.

Please register and go to the Tuwel forum to see more information.

First meeting on March 1st, at 2pm lecture room Zemanek (ground floor).

ECTS Breakdown

36 h: Lectures
68 h: Solving and presenting the solutions of 2-3 exercise sheets
22 h: Preparation and follow-up
23.5 h: Exam preparation
0.5 h: Exam
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Sum: 150 h
ECTS: 6

Literature

• F. Brandt, V. Conitzer, U. Endriss, J. Lang, and A. D. Procaccia, ed.:Handbook of Computational Social Choice. Cambridge University Press, 2015.
• U. Endriss, ed: Trends in Computational Social Choice. AI Access, 2017
• D. Gusfield and R. Irving: The Stable Marriage Problem--Structure and Algorithms. MIT Press, 1989
• D. Manlove: Algorithmics of Matchihng under Preferences, World Scientific Press, 2013
• 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

Exercise + oral exam

Basic knowledge of algorithmic design. Good to have heard:

1. Algorithmen und Datenstrukturen,
2. Algorithmics,
3. Komplexitätstheorie, etc.