Is Computational Redistricting and Algorithms the Solution to Gerrymandering?

Speaker:  Sheldon H Jacobson – Urbana, IL, United States
Topic(s):  Society and the Computing Profession


Every 10 Years, the outcome of the United States Census leads to a reallocation of congressional seats, typically requiring state legislatures to redesign their congressional districts.  This creates the opportunity for these states to design districts that favor a particular party, the process of gerrymandering in political redistricting.  This presentation discusses how gerrymandering is achieved, examples of such efforts, and how algorithms can be used to mitigate its proliferation.   In particular, political redistricting is treated as a multi-criteria problem with conflicting objectives (based onmetrics like compactness, population balance, efficiency gaps, and partisan asymmetry). Many of these metrics have received significant attention, though they remain controversial as to which such metrics are best suited to define fair district maps. This research uses a multi-objective optimization approach to reveal obstacles in defining fair district maps. The results obtained challenge a number of common perceptions of redistricting, suggesting that defining fair maps may not only be extremely difficult, but also, simply unrealistic.

About this Lecture

Number of Slides:  45
Duration:  50 minutes
Languages Available:  English
Last Updated: 

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