Combating Gerrymandering with Ranked Choice Voting: An Experimental Analysis of Multimember Districts in the United States

• 成果类型: Article; Early Access
• 署名作者: Garg, Nikhil; Gurnee, Wes; Rothschild, David; Shmoys, David B.
• 署名单位: Cornell University; Massachusetts Institute of Technology (MIT); Microsoft
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2024.1167
• 发表日期: 2026-06-25
• 关键词: redistricting; gerrymandering; multimember districts; ranked-choice voting; proportional representation; social choice; integer programming; MULTI-MEMBER DISTRICTS; SINGLE-MEMBER; COMPUTATIONAL APPROACH; REPRESENTATION; constituencies; optimization; candidates; turnout; rules

摘要

Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States-winnertake-all single-member districts-both enables substantial partisan gerrymandering and constrains fair redistricting, preventing proportional representation in legislatures. We study the design of multimember districts (MMDs), in which each district elects multiple representatives, potentially through a non-winner-take-all voting rule. We carry out largescale empirical analyses for the U.S. House of Representatives under MMDs with different social choice functions and algorithmically generated maps optimized for either partisan benefit or proportionality. Doing so requires efficiently incorporating predicted partisan outcomes-under various multiwinner social choice functions-into an algorithm that optimizes over an ensemble of maps. We find that, with three-member districts using single transferable vote, fairness-minded independent commissions would be able to achieve proportional outcomes in every state up to rounding, and advantage-seeking partisans would have their power to gerrymander significantly curtailed. Simultaneously, such districts would preserve geographic cohesion. Through simulation, we find that the insights are robust to cross-party voting. In the process, we advance a rich research agenda at the intersection of social choice and computational gerrymandering.