Monte Carlo sampling is generally a numerical method of solving mathematical problems through random sampling. When applied to redistricting, many academics advocate its use either to ferret out extreme gerrymandering or to produce more fairly drawn maps in the first place. Professor of Mathematics Jonathan Christopher Mattingly of Duke University, will be discussing the Monte Carlo method in-depth as it applies to redistricting on July 20th as the I.E. Block Community Lecturer at the Society for Industrial and Applied Mathematics (SIAM) virtual annual meeting.
Mattingly has served as an expert witness in several high-profile restricting cases, including the recent U.S. Supreme Court case Rucho v. Common Cause. The lecture is open to the public and will be livestreamed on YouTube. Register for Mattingly’s talk here.
Still need a little clarity on Monte Carlo methodology in redistricting? Here is a plain language explainer.
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