On the exact amount of missing information that makes finding possible winners hard

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-08-01 DOI:10.1016/j.jcss.2023.02.003
Palash Dey , Neeldhara Misra
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Abstract

In the possible winner problem, we need to compute if a set of partial votes can be completed such that a given candidate wins the election under some specific voting rule. In this paper, we determine the smallest number of undetermined pairs per partial vote for which the Possible Winner problem is NP-complete. In particular, we find the exact values of t for which the Possible Winner problem transitions to being NP-complete from being in P, where t is the maximum number of undetermined pairs in every vote. We demonstrate tight results for a broad class of scoring rules, Copelandα for every α[0,1], maximin, and Bucklin voting rules. A somewhat surprising aspect of our results is that for many of these rules, the Possible Winner problem turns out to be hard even if every vote has at most one undetermined pair of candidates.

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缺失信息的确切数量使得找到可能的赢家变得困难
在可能的获胜者问题中,我们需要计算一组部分选票是否可以完成,从而使给定的候选人在特定的投票规则下赢得选举。在本文中,我们确定了每个部分投票的最小数量的未确定对,其中可能的赢家问题是NP完全的。特别地,我们找到了t的精确值,对于该值,可能的赢家问题从处于P中转变为NP完全,其中t是每次投票中未确定对的最大数量。我们证明了一类广泛的评分规则的严密结果,Copelandα对于每个α∈[0,1],maximin和Bucklin投票规则。我们的结果有点令人惊讶的一点是,对于其中许多规则,即使每张选票最多有一对未确定的候选人,“可能的赢家”问题也很难解决。
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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