Randomized Algorithms and Representative Democracy

For the last century, the U.S. Supreme Court has sought a manageable approach to ensuring that all voters are weighted equally in elections. When a redistricting plan is challenged for vote dilution, you need to know how a non-gerrymandered plan would be different.In this Presidential Lecture, Moon Duchin will describe a novel use of graph algorithms to build a non-gerrymandered baseline for redistricting. Beyond that, she will discuss new mathematical ideas for the broader problem of understanding electoral systems, including how to measure vote weight. These ideas can help build toward a “science of democracy” for policymakers and lawmakers.

Duchin is a professor of computer science and data science at the University of Chicago. Her background in pure math centers on geometry, topology, groups and dynamics and her applied work uses these tools to build algorithms and models to study the mechanisms of democracy. She runs a multidisciplinary lab that brings math and computing into conversation with law, policy, and geography. Duchin has served as an expert in numerous voting rights court cases around the country, and her scholarly work has been recognized with an NSF CAREER award, a Guggenheim Fellowship, a Radcliffe Fellowship, a Sloan Professorship at SLMath (formerly the Mathematical Sciences Research Institute) and election as a fellow of the American Mathematical Society. She is also an external faculty member at the Santa Fe Institute, which is dedicated to the study of complex systems.