基于批准的委员会在不完整信息下的投票

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aviram Imber , Jonas Israel , Markus Brill , Benny Kimelfeld
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引用次数: 0

摘要

我们研究了基于批准的委员会投票与不完全信息的批准偏好的选民。我们考虑了几种不完备模型,其中每个选民将候选人集划分为批准的,不批准的和未知的候选人,可能在后一类候选人中有顺序的偏好约束。这捕获了选民没有评估所有候选人和/或不知道选民在批准和不批准候选人之间划分阈值的情况。我们研究了一些经典的基于批准的委员会投票规则(包括比例批准投票和Chamberlin-Courant)的一些基本计算问题的复杂性。这些问题包括确定一组给定的候选人是可能的还是必要的获胜委员会,以及给定的候选人是否可能或必须是获胜委员会的成员。我们还考虑比例代表制公理和决定一个给定委员会是否可能或必然具有代表性的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approval-based committee voting under incomplete information
We investigate approval-based committee voting with incomplete information about the approval preferences of voters. We consider several models of incompleteness where each voter partitions the set of candidates into approved, disapproved, and unknown candidates, possibly with ordinal preference constraints among candidates in the latter category. This captures scenarios where voters have not evaluated all candidates and/or it is unknown where voters draw the threshold between approved and disapproved candidates. We study the complexity of some fundamental computational problems for a number of classic approval-based committee voting rules including Proportional Approval Voting and Chamberlin–Courant. These problems include determining whether a given set of candidates is a possible or necessary winning committee and whether a given candidate is possibly or necessarily a member of the winning committee. We also consider proportional representation axioms and the problem of deciding whether a given committee is possibly or necessarily representative.
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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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