Project Description
How do we draw “fair” electoral districts? Given a districting, can we quantify the extent of gerrymandering? In this project we will design and implement Markov chain Monte Carlo (MCMC) algorithms to generate unbiased random redistricting plans. We will then use these MCMC algorithms to hopefully develop a tool to quantify how fair a given districting is.
I will teach the group the basics about Markov chains and graph theory, but interested students should have a strong background in discrete math, including an A in CS 40 (or Math 8) and PSTAT 120A. If you haven't taken CS 130A yet then you need to be with Prof. Vigoda in Fall '24 as there will be a fair amount of graph theory involved. There will also be a strong programming component especially since the space of possible redistrictings is extremely complex and high-dimensional. Moreover, designing a Markov chain which converges quickly, and empirically testing its convergence are also challenging aspects we will need to consider.
Team Members
- Lucas Solomon
- Mathias Ooi
- Sanjay Srikanth
- Ysabel Chen
Professor and Mentors
- Prof. Eric Vigoda
- Grad mentors: Stuart Wayland