Social Choice and Preference Aggregation - 096578
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Lecture |
Exercise |
Laboratory |
Project or
Seminar |
House
Work |
Weekly
Hours |
2 |
1 |
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3 |
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Determination of the grade according to progress during the semester and the submission of the final thesis
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Prerequisites:
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(
| | Introduction to Algorithms |
094226
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and
| Probability (Ie) |
094411
| ) |
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or
|
(
| | Data Structures and Algorithms |
094224
| |
| | |
and
| Probability (Ie) |
094411
| ) |
|
or
|
(
| | Data Structures and Algorithms |
094224
| |
| | |
and
| Probability (Advanced) |
094412
| ) |
|
or
|
(
| | Probability (Advanced) |
094412
| |
| | |
and
| Algorithms 1 |
234247
| ) |
|
or
|
(
| | Probability Theory |
104222
| |
| | |
and
| Algorithms 1 |
234247
| ) |
|
or
|
(
| | Int. to Data Structur and Algorithms |
044268
| |
| | |
and
| Introduction to Probability H |
104034
| ) |
|
or
|
(
| | Probability Theory |
104222
| |
| | |
and
| Combinatorial Algorithms |
104291
| ) |
Axioms of Social Choice: May S Theorem, Common Voting Rules, Pagerank, Condorcet S Paradox and Arrow S Theorem. Voting Rules as Maximum-Likelihood Estimators. Preference Structures: Single-Peaked and Combinatorial Preferences, Generative Models with Ground Truth (Placket-Luce) and Without (Urn). Strategic Voting: Gibbard-Satterthwaite Theorem, Complexity Barriers and Vcg, Equilibrium Models, Heuristics, Iterative Voting and Convergence. Homework Will Be in the Python Programming Language.
Learning Outcomes
At the End of the Course the Students:
1. Will Know to Understand the Conceptual, Computational, and Strategic Challenges of Aggregating Preferences.
2.
Will Be Able to Spot Problems and Underlying Assumptions of Known and New Voting Mechanisms.
3.
Will Be Able to Choose and/Or Design An Appropriate Mechanism for Aggregating Preferences in Various Contexts.
Textbooks| Published | Publisher | Authors | Book |
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| 2016 | cambridge university press | f. brandt, v. conitzer, u. endriss, j. l | handbook of computational social choice |
Created in 08/03/2021 Time 14:48:52
