Primarily about primaries

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Allan Borodin , Omer Lev , Nisarg Shah , Tyrone Strangway
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引用次数: 0

Abstract

Much of the social choice literature examines direct voting systems, in which voters submit their ranked preferences over candidates and a voting rule picks a winner. Real-world elections and decision-making processes are often more complex and involve multiple stages. For instance, one popular voting system filters candidates through primaries: first, voters affiliated with each political party vote over candidates of their own party and the voting rule picks a set of candidates, one from each party, who then compete in a general election.

We present a model to analyze such multi-stage elections, and conduct what is, to the best of our knowledge, the first quantitative comparison of the direct and primary voting systems in terms of the quality of the elected candidate, using the metric of distortion, which attempts to quantify how far from the optimal winner is the actual winner of an election. Our main theoretical result is that voting rules (which are independent of party affiliations, of course) are guaranteed to perform in the primary system within a constant factor of the direct, single stage setting. Surprisingly, the converse does not hold: we show settings in which there exist voting rules that perform significantly better under the primary system than under the direct system. Using simulations, we see that plurality benefits significantly from using a primary system over a direct one, while Condorcet-consistent rules do not.

主要涉及初选
大部分社会选择文献研究的是直接投票系统,即选民提交他们对候选人的排序偏好,然后由投票规则选出获胜者。现实世界中的选举和决策过程往往更为复杂,涉及多个阶段。例如,一种流行的投票系统通过初选筛选候选人:首先,隶属于各政党的选民对本党派的候选人进行投票,投票规则选出一组候选人,每个党派选出一名,然后由这些候选人在大选中竞争。我们提出了一个模型来分析这种多阶段选举,并就我们所知,首次就当选候选人的质量对直接投票系统和初选投票系统进行了定量比较,使用的指标是失真度,它试图量化选举的实际获胜者离最优获胜者有多远。我们的主要理论结果是,投票规则(当然与党派无关)在初选系统中的表现保证在直接、单一阶段设置的恒定系数之内。令人惊讶的是,相反的情况并不成立:我们展示了在初选制下投票规则的表现明显优于直接制的情况。通过模拟,我们发现使用初选制比使用直接制更能使多数票获益,而康德赛特一致性规则则不然。
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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