Quantifying Gerrymandering: Separating Natural Bias from Partisan Bias in Redistricting
Abstract: In considering political districts, how do we distinguish between natural geopolitical bias and partisan gerrymandering that disenfranchises voters? My research group at Duke University seeks to answer this question using Markov Chain Monte Carlow algorithms that sample the space of district plans. In sampling this space, we generate an ensemble made of thousands of a-political district plans that comply with traditional redistricting criteria. When the ensemble is combined with historical election data, we reveal a range of typical election outcomes. We then compare the range typical outcomes with an enacted plan to determine whether the enacted plan is an extreme outlier and therefore a partisan gerrymander. This approach has swayed both the Pennsylvania Supreme Court and a North Carolina federal district court; the latter case relied heavily on our research. There are three principle challenges of this work: (1) to develop redsitricting criteria, (2) to sample the space of redistricting plans that meet this criteria, and (3) analyze the ensemble of sampled plans. Although the first challenge is primarily a legal and political challenge, the latter two challenges provide a rich environment for applied mathematics that I will discuss in this talk.
Refreshments in Math Commons Room at 3:30pm