- Lecturer: Eric Pacuit (website)
- Venue: Leuven, Belgium
TBA

- Dates: August 5 - August 9, 2024
- Meeting Times: TBA
This course is an introduction to social choice theory, with a special focus on the use of machine learning to study voting methods. The first part of the course will introduce the mathematical analysis of voting methods, including probabilistic voting methods and new voting paradigms such as liquid democracy. This introduction to voting theory will include hands-on experience with the Python package pref_voting, a set of tools in Python designed to facilitate the computational analysis of voting methods. The main objective of the course is to explore the way that machine learning tools and ideas have been used to complement existing social-choice theoretic results. The main topics that will be discussed include:

- introduction to mathematical analysis of voting methods
- probabilistic voting methods
- liquid democracy
- voting paradoxes
- quantitative analysis of voting methods (e.g., Condorcet efficiency)
- the PAC-learnability of voting rules
- learning voting rules using neural networks (multi-layer perceptrons)
- using modern deep learning techniques to generate synthetic but realistic election data
- strategic voting
- quantitative analysis of strategic voting (e.g., the Nitzan-Kelly index of a voting method)
- learning to successfully manipulate voting rules based on limited information about how the other voters will vote using neural networks (multi-layer perceptrons)
- using large-language models to improve group decision-making

While most of the course will focus on applications of machine learning in social choice theory, time permitting, potential application of social choice theory in machine learning will also be discussed.

This course will be self-contained. No previous experience with voting theory or machine learning will be assumed. All of the topics in voting theory (e.g., voting methods, voting paradoxes, strategic voting) and ideas from machine learning (e.g., PAC-learning, multi-layer perceptrons, etc.) will be introduced.

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