用排序投票对抗选区划分不公：美国多席位选区的实验分析
Combatting Gerrymandering with Ranked Choice Voting: An Experimental Analysis of Multi-member Districts in the United States
AI总结：
通过实验分析多席位选区中不同投票规则的效果，发现使用单一可转移投票的三席位选区能在实现比例代表制的同时显著削弱党派划分选区的能力。
摘要：
每个代议制民主国家都必须指定一种机制，选民通过该机制选择其代表。美国最常见的机制——赢者通吃的单一席位选区——既助长了严重的党派选区划分，又限制了“公平”的重新划分选区，阻碍了立法机构中的比例代表制。我们研究 extit{多席位选区（MMDs）}的设计，其中每个选区选举多名代表，可能通过非赢者通吃的投票规则。我们针对美国众议院，在不同社会选择函数下的多席位选区中，以及针对党派利益或比例性优化的算法生成地图，进行了大规模实证分析。这需要将预测的党派结果（在各种多赢家社会选择函数下）高效地纳入优化地图集合的算法中。我们发现，使用单一可转移投票的三席位选区，注重公平的独立委员会能够在每个州实现比例结果（四舍五入后），而寻求优势的党派划分选区的能力将显著受限。同时，此类选区将保持地理凝聚力。通过模拟，我们发现这些见解对跨党派投票具有鲁棒性。在此过程中，我们推进了社会选择与计算性选区划分交叉领域的一个丰富研究议程。
英文摘要：
Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States -- Winner takes all single-member districts -- both enables substantial partisan gerrymandering and constrains `fair' redistricting, preventing proportional representation in legislatures. We study the design of \textit{multi-member districts (MMDs)}, in which each district elects multiple representatives, potentially through a non-Winner takes all voting rule. We carry out large-scale empirical analyses for the U.S. House of Representatives under MMDs with different social choice functions, under algorithmically generated maps optimized for either partisan benefit or proportionality. Doing so requires efficiently incorporating predicted partisan outcomes -- under various multi-winner 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, \textit{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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
