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Gerrymandering and computational redistricting.

Olivia Guest1, Frank J Kanayet2, Bradley C Love1,3

  • 11Department of Experimental Psychology, University College London, London, UK.

Journal of Computational Social Science
|October 22, 2019
PubMed
Summary
This summary is machine-generated.

Computational models can optimize districting criteria for fairer representation, addressing partisan gerrymandering. Machine-drawn districts show superior optimization and emergent properties compared to human efforts.

Keywords:
Cognitive limitationsComputational redistrictingGerrymanderingWeighted k-means

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Area of Science:

  • Political Science
  • Computer Science
  • Computational Social Science

Background:

  • Partisan gerrymandering threatens democratic principles by manipulating electoral district boundaries.
  • The complexity of creating fair electoral districts can surpass human cognitive abilities.

Purpose of the Study:

  • To develop and evaluate a computational model for automating the districting process.
  • To optimize objective and transparent criteria for drawing electoral districts, focusing on spatial compactness.

Main Methods:

  • Formulated a computational model to minimize pairwise distances between voters within a district.
  • Utilized US Census Bureau data to test the model's predictions on district compactness.

Main Results:

  • The discrepancy in compactness between computed and actual districts was largest in large states, indicating human limitations in complex districting.
  • Machine-optimized solutions exhibited greater overall optimization and emergent properties compared to human-drawn districts.

Conclusions:

  • Computational models offer a viable solution for optimizing districting criteria and mitigating partisan gerrymandering.
  • A division of labor, with humans defining criteria and machines optimizing boundaries, can improve the districting process.