Voting according to one’s political stances is difficult: Problems definition, computational hardness, and approximate solutions

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Aitor Godoy , Ismael Rodríguez , Fernando Rubio
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Abstract

This paper studies the computational complexity of two voting problems where the goal is deciding how a given voter should vote to favour their personal stances. In the first problem, given (a) the voter stance towards each law that will be voted by the parliament and (b) the political stance of each party towards each law (all party members are assumed to vote according to it), the goal is finding the parliamentary seats distribution maximizing the number of laws that will be approved/rejected as desired by the voter. In the second problem no parliament is involved, but a single issue with several possible answers is voted by citizens in a presidential election with several candidates. The problem consists in deciding how a group of voters, split in different electoral districts, all of them supporting the same candidate, should vote to make their candidate president. It is assumed that (a) all delegates of each electoral district are assigned to the candidate winning in the district, (b) after the election day, candidates may ask their assigned delegates to support other candidates receiving more votes than them, and these post-electoral supporting stances are known in advance by the electorate, and (c) the group of voters that is coordinated knows the votes that will be cast by the rest of the electorate. For each problem, its NP-hardness as well as its inapproximability are proved. This implies that something as essential as exercising the democratic right to vote, in such a way that the voting choice will be the best for the voter’s political stances, is at least NP-hard. It is also shown how genetic algorithms can be used to obtain reasonable solutions in practice despite the limitations of theoretical approximation hardness.

根据自己的政治立场投票是很困难的:问题定义、计算难度和近似解
本文研究了两个投票问题的计算复杂性,这两个问题的目标都是决定特定选民应如何投票以支持其个人立场。在第一个问题中,给定(a) 选民对议会将表决的每项法律的立场和(b) 各政党对每项法律的政治立场(假定所有政党成员都会按照该立场投票),目标是找到议会席位分配,最大限度地增加按选民意愿批准/否决的法律数量。在第二个问题中,不涉及议会,而是由公民在有多名候选人的总统选举中投票决定一个有多个可能答案的单一问题。问题在于,一群选民被分在不同的选区,他们都支持同一个候选人,应该如何投票使他们的候选人成为总统。假定:(a) 每个选区的所有代表都被分配给在该选区获胜的候选人;(b) 选举日后,候选人可能会要求其分配的代表支持得票比自己多的其他候选人,而这些选举后的支持立场是选民事先知道的;(c) 被协调的选民群体知道其他选民将投出的选票。对于每个问题,都证明了其 NP 难度和不可逼近性。这意味着,像行使民主投票权这样重要的事情,要使投票选择最符合选民的政治立场,至少是 NP 难的。研究还表明,尽管存在理论上的近似难度限制,遗传算法在实践中仍可用于获得合理的解决方案。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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