A Computational Approach to Measuring Vote Elasticity and Competitiveness
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DOI: 10.1080/2330443X.2020.1777915
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Cited by:
- Sarah Cannon & Ari Goldbloom-Helzner & Varun Gupta & JN Matthews & Bhushan Suwal, 2023. "Voting Rights, Markov Chains, and Optimization by Short Bursts," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-38, March.
- Amariah Becker & Dara Gold, 2022. "The gameability of redistricting criteria," Journal of Computational Social Science, Springer, vol. 5(2), pages 1735-1777, November.
- Benadè, Gerdus & Ho-Nguyen, Nam & Hooker, J.N., 2022. "Political districting without geography," Operations Research Perspectives, Elsevier, vol. 9(C).
- Jeanne Clelland & Haley Colgate & Daryl DeFord & Beth Malmskog & Flavia Sancier-Barbosa, 2022. "Colorado in context: Congressional redistricting and competing fairness criteria in Colorado," Journal of Computational Social Science, Springer, vol. 5(1), pages 189-226, May.
- Swamy, Rahul & King, Douglas M. & Ludden, Ian G. & Dobbs, Kiera W. & Jacobson, Sheldon H., 2024. "A practical optimization framework for political redistricting: A case study in Arizona," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
- Kiera W. Dobbs & Rahul Swamy & Douglas M. King & Ian G. Ludden & Sheldon H. Jacobson, 2024. "An Optimization Case Study in Analyzing Missouri Redistricting," Interfaces, INFORMS, vol. 54(2), pages 162-187, March.
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