Seminar Details
Metric Distortion in Social Choice
- Start Date: September 4, 2024
- Event Start Time: 11:00 AM
- Event End Time: 12:00 PM
- Seminar Series: Theoretical Computer Science Seminar
- Presenter(s): Kangning Wang - Rutgers University
- Event Location: Conference Room 301 | Rutgers University | CoRE Building | 96 Frelinghuysen Road
- Event Additional Info: <p>See: <a href="https://theory.cs.rutgers.edu/theory_seminar">https://theory.cs.rutgers.edu/theory_seminar</a></p>
- Presentation Type: Stand Alone Presentation
- Abstract:
This talk will be about metric distortion in social choice. I will cover several major results (by us and others) in this area, and discuss open questions and directions.
In a standard social choice scenario, voters express their preferences over candidates through votes, and then a voting rule aggregates those votes and selects one winning candidate. These votes can often be in the ranked-choice form, meaning that each voter submits her ranking of all the candidates. How efficient/effective can ranked-choice voting be?
In the metric distortion framework, we assume all voters and candidates reside in the same metric space, and each voter bears a cost equal to the distance between her and the elected candidate. The goal is to minimize the sum of costs over the voters. Various classical ranked-choice voting rules and novel ones can give the following guarantee: its sum of costs will be at most a constant times the true optimal sum of costs. Improving this guarantee has been the center of this research area, and this talk will review the progress.