Application of Linear Programming in Gerrymandering for Optimal Partisan Gain in the United States

Jingze Dai

2025

Abstract

This study investigates how linear programming can be applied to engineer optimal electoral districting for partisan advantage, focusing on the 2012 Pennsylvania House of Representatives election as a case study. Although the Republican Party received only 49.9% of the statewide popular vote, they secured 13 out of 18 congressional seats through strategic districting. Using county-level voting and population data, this paper develops a linear programming model in Python to simulate and optimize gerrymandering for the Republican Party. The model, even with relaxed constraints on contiguity and compactness, demonstrates the ability to secure up to 17 out of 18 seats for the party-an outcome more extreme than the real-world result. The model validates core gerrymandering strategies such as “packing” and “cracking,” revealing how districts can be drawn to amplify seat share far beyond vote share. This paper also compares the program’s output to the actual 2012 district map and discuss the similarities in partisan tactics despite differing structural constraints. The findings underscore the effectiveness of linear programming in modeling partisan manipulation and offer insights into the limits and consequences of gerrymandering under district-based majoritarian systems.

Paper Citation

in Harvard Style

Dai J. (2025). Application of Linear Programming in Gerrymandering for Optimal Partisan Gain in the United States. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 474-481. DOI: 10.5220/0014361400004718

in Bibtex Style

@conference{emiti25,
author={Jingze Dai},
title={Application of Linear Programming in Gerrymandering for Optimal Partisan Gain in the United States},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={474-481},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014361400004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Application of Linear Programming in Gerrymandering for Optimal Partisan Gain in the United States
SN - 978-989-758-792-4
AU - Dai J.
PY - 2025
SP - 474
EP - 481
DO - 10.5220/0014361400004718
PB - SciTePress