Social Choice and Preference Aggregation  096578





Lecture 
Exercise 
Laboratory 
Project or Seminar 
House Work 
Weekly Hours 
2 
1 


3 

Determination of the grade according to progress during the semester and the submission of the final thesis
Prerequisites:
 
(
  Introduction to Algorithms 
094226
 
  
and
 Probability (Ie) 
094411
 ) 

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 MaximumLikelihood Estimators. Preference Structures: SinglePeaked and Combinatorial Preferences, Generative Models with Ground Truth (PlacketLuce) and Without (Urn). Strategic Voting: GibbardSatterthwaite 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.
TextbooksPublished  Publisher  Authors  Book 

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