Title:
Quantifying Gerrymandering using Markov Chain Monte Carlo Algorithms
Quantifying Gerrymandering using Markov Chain Monte Carlo Algorithms
Author(s)
Wahal, Samarth
Advisor(s)
Vigoda, Eric
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
We look at the rules and regulations surrounding redistricting in the United State. We examine Markov Chain Monte Carlo algorithms that are able to sample redistricting plans adhering to these rules. We implement the algorithm proposed by Fifield et al. [11] and use it to sample plans for the state of Georgia. We count the number of Republican House seats won for each sampled plan. We compare Georgia’s existing redistricting plan to this distribution in order to test the null hypothesis that Georgia is not gerrymandered. Our results show that we fail to reject this null hypothesis.
Sponsor
Date Issued
2019-12
Extent
Resource Type
Text
Resource Subtype
Undergraduate Thesis