Time: Monday, November 22, 5:30-6:30pm ET, followed by a 15 minute Q&A Speaker: Ben Blum-Smith, NYU Center for Data Science These algorithms are currently being employed to analyze and audit plans in the 2020 redistricting cycle. He has also developed several new algorithms to sample and understand the space of redistricting plans. Covington, and was featured in an Amicus brief submitted to the Supreme Court during Gill v. This work was incorporated into expert testimony in Common Cause v. He began working with Jonathan Mattingly on algorithms to quantify gerrymandering in 2016 within this collaboration, Greg has analyzed and quantified gerrymandering in the North Carolina congressional districts, the Wisconsin general assembly, and the North Carolina legislature, and more. Since then, he has been in a research position at Duke University that has spanned both the Departments of Biomedical Engineering and Mathematics. in mathematics from UNC Chapel Hill in 2013. These sampling methods provide rich grounds both for mathematical exploration and development and also serve as a practical and relevant algorithm that can be employed to establish and maintain fair governance.īio: Gregory Herschlag received his Ph.D. I will also mention several open problems and challenges in this field. I will discuss the sampling methods we employ and discuss several recent algorithmic advances. In this talk, I will discuss how our research group at Duke has analyzed gerrymandering. The first step, though largely a legal question of compliance, provides interesting grounds for mathematical translation between policies and probability measures the second and third points create rich problems in the fields of applied mathematics (sampling theory) and data analysis, respectively. The process relies on three distinct components: First, we determine rules for compliant redistricting plans along with codifying preferences between these plans next, we sample the space of compliant redistricting plans (according to our preferences) and generate a large collection of non-partisan alternatives finally, we compare the collection of plans to a particular plan of interest. The basic idea behind these methods is to compare a given set of districts to a large collection of neutrally drawn plans. Although we have seen increasingly precise and effective gerrymanders, a number of mathematicians, political scientists, and lawyers are developing effective methodologies at uncovering and understanding the effects of gerrymandered districts. Title: Quantifying Gerrymandering: Advances in Sampling Graph Partitions from Policy-Driven MeasuresĪbstract: Gerrymandering is the process of manipulating political districts either to amplify the power of a political group or suppress the representation of certain demographic groups. Place: Zoom meeting - contact seminar organizers for details Speaker: Gregory Herschlag, Duke University We invite you to tune in to Zoom instead (link sent out via email, or contact Ben Blum-Smith ( with any questions, including speaker suggestions, or to subscribe to the email list. [**NOTE: These seminars have gone remote until further notice. The seminar is held on select Mondays and Wednesdays at the Center for Data Science at 60 5th Ave. The scope is not limited to these and is expected to expand as further applications emerge. The purpose of the seminar is to stimulate mathematical activity on problems relating to democracy, and to foster interdisciplinary collaboration between mathematicians and other scholars and democratic stakeholders.Įxamples of topics of interest include detection of gerrymandering, fairness and accountability of algorithms used in social decision-making, voting and apportionment theory, applications of statistics to discrimination law and the census, and mathematical modeling of democratic processes. The Math and Democracy Seminar features research on contact points between the mathematical sciences and the structure of democratic society.
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