This article highlights iiD faculty member Jonathan Mattingly's work mathematically dissecting the structure of a typical redistricting to identify gerrymandering.
Detecting Gerrymandering with Mathematics
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This article highlights iiD faculty member Jonathan Mattingly's work mathematically dissecting the structure of a typical redistricting to identify gerrymandering.
Sophie Guo, Math/PoliSci major, Bridget Dou, ECE/CompSci major, Sachet Bangia, Econ/CompSci major, and Christy Vaughn spent ten weeks studying different procedures for drawing congressional boundaries, and quantifying the effects of these procedures on the fairness of actual election results.
A team of students led by Professors Jonathan Mattingly and Gregory Herschlag will investigate gerrymandering in political districting plans. Students will improve on and employ an algorithm to sample the space of compliant redistricting plans for both state and federal districts. The output of the algorithm will be used to detect gerrymandering for a given district plan; this data will be used to analyze and study the efficacy of the idea of partisan symmetry. This work will continue the Quantifying Gerrymandering project, seeking to understand the space of redistricting plans and to find justiciable methods to detect gerrymandering. The ideal team has a mixture of members with programing backgrounds (C, Java, Python), statistical experience including possibly R, mathematical and algorithmic experience, and exposure to political science or other social science fields.
Read the latest updates about this ongoing project by visiting Dr. Mattingly's Gerrymandering blog.