[gov3009-l] Gov 3009 Applied Statistics Workshop (11/11) -- Cory McCartan

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, November 11, where we will hear Cory McCartan (Harvard University) presents research on "Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans." *Abstract*: Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to large maps with many districts, incorporate realistic legal constraints, and accurately sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these three areas. We present a new Sequential Monte Carlo (SMC) algorithm that draws representative redistricting plans from a realistic target distribution of choice. Because it yields nearly independent samples, the SMC algorithm can efficiently explore the relevant space of redistricting plans than the existing Markov chain Monte Carlo algorithms that yield dependent samples. Our algorithm can simultaneously incorporate several constraints commonly imposed in real-world redistricting problems, including equal population, compactness, and preservation of administrative boundaries. We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated. We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the state of Pennsylvania. Open-source software is available for implementing the proposed methodology. Paper is available from here <https://arxiv.org/pdf/2008.06131.pdf>. *Zoom link*: https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/