Math in the City, a capstone course currently focused on gerrymandering and offered by the Department of Mathematics, will present a mini-conference of four talks from 2:30 to 4:30 p.m. Dec. 6 in Oldfather Hall, Room 204.
Partisan manipulation of voting districts, or gerrymandering, is a topic of enormous recent interest. In the past year alone, the United States has seen several major courts render decisions on the issue. However, many state and federal courts have been unwilling to rule on gerrymandering cases, believing there to be no reliable metric for whether a districting plan has been gerrymandered.
Numerous metrics have been proposed to address gerrymandering. The department’s capstone course has studied four such metrics, using them to identify gerrymandering and determine whether districting plans can be designed to be less partisan.
The schedule of talks includes:
2:30 p.m., “Geometric Metrics for Districting Plans” — Nick Bouda, Bret Reetz, Judd Salem, Lane Weidner and Qichang Yang will discuss how the shapes of voting districts can indicate partisan bias before delving into how certain metrics may determine whether districting plans represent such bias.
3 p.m., “Using Votes-Seats Curves to Study Election Bias” — Tara Brockman, Fatima Barragan Herrera, August McClenahan, Tianyu Shen and Wei Young Tian will explain mathematical curves that can describe a solid indicator of whether an election will be perceived as fair: the relationship between how many votes a party gets and how many seats it wins.
3:30 p.m., “The Efficiency Gap and Related Metrics” — Jean Claude Bamute Kamba, Shuhoa Mu, Hannah Oh, Taylor Price and Thu Vu will discuss metrics that measure unfair biases in “wasted” votes, which may be a sign of gerrymandering when one party’s wasted-vote count significantly exceeds the other’s.
4 p.m., “Markov Chain Monte Carlo Methods for Detecting Gerrymandering” — To determine whether a districting plan is gerrymandered, analysts could compare it to all possible districting plans. However, there are way too many possible districting plans to do this is practice, making it important to find a representative sample of all districting plans. Masen Bachleda, Roderick Riley, Nicholas Verdoni and Geigh Zollicoffer will explain how so-called Markov chain Monte Carlo methods manage this, presenting a computer program for a simple election to illustrate the principles.
