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arXiv AI · 2026/7/30 17:37:14

Algorithms for Structured Elections under Thiele Voting Rules

AI 中文解读
核心亮点:这项研究为选举投票规则的计算难题找到了更高效的算法,尤其让“比例代表制”在特定场景下不再难以计算。通俗解读:想象大家投票选委员会,每个人可以支持多个候选人,但如何公平选出最符合大家意愿的组合是个数学难题。这次科学家发现,当选民按顺序排列、每个候选人只获得连续一批选民支持时,可以用更聪明的方法快速算出结果,就像给难题找到了捷径。实际影响:这项成果看似离生活很远,但审批推荐系统、广告组合优化甚至AI训练数据筛选都会用到类似的投票算法。未来你参与线上投票、平台自动挑选最佳方案时,计算会更快更聪明,而且规则更公平透明。研究还解决了两个多年悬而未决的数学问题,为人工智能决策提供了可靠工具,让复杂选择不再靠碰运气。
We study the computational complexity of winner determination problems in approval-based committee elections under Thiele voting rules. These form a class of rules parameterized by a fixed weight vector that specifies how a voter's satisfaction depends on the number of approved candidates elected. We first analyze the structure of optimal solutions based on the sets of voters who approve each candidate---that is, how voters' approval ballots induce dependencies between candidates---revealing constraints on a winning committee under any fixed Thiele voting rule. Using this, we design FPT algorithms for Proportional Approval Voting (PAV) and other Thiele rules on a natural restricted domain known as the Voter Interval (VI) domain---that is, after a suitable ordering of voters, each candidate is approved by a consecutive interval of voters. In particular, we show that every Thiele rule on VI is FPT with respect to a parameter for which the problem is NP-hard on general instances, even when the parameter takes constant values. Our results advance the understanding of the computational complexity of PAV on Voter Interval instances, which remains one of the central open questions in this area. We further resolve two open questions from the literature on PAV (and other Thiele voting rules) by providing a polynomial-time algorithm for instances where each candidate is approved by at most two voters, and an FPT algorithm parameterized by the total score of a winning committee.
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