具有恒定 $$\beta $$ 的空间投票游戏中的多元性

Pub Date : 2024-01-03 DOI:10.1007/s00454-023-00619-5
Arnold Filtser, Omrit Filtser
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引用次数: 0

摘要

考虑一组选民 V,由度量空间 (X, d) 中的多集表示。如果对于任何其他选择(q\in X)都成立,那么对于V来说,一个选择(p\in X)就被称为plurality point,即(|\{v\in V\mid \beta \cdot d(p,v)\le d(q,v)\}| \ge \frac{|V|}{2}\).换句话说,至少有一半的投票者 "更喜欢 "p而不是q,当\(\beta \)的额外因子被用于支持p时。对于\(\beta =1\),这等同于孔多塞赢家,而孔多塞赢家很少存在。Aronov, de Berg, Gudmundsson 和 Horton [TALG 2021]提出了 \(\beta \)-plurality的概念,作为对Condorcet准则的一种放松。让 \beta ^*_{(X,d)}=\sup \{beta \mid \text{ every }\(有限的)\(多集)V{ in}X{ admitsa } (plurality)\点)。参数 \(\beta^*\)决定了达到稳定决策所需的放松程度。阿罗诺夫等人的研究表明表明,对于欧几里得平面,(\beta ^*_{({\mathbb {R}}^2,\Vert \cdot \Vert _2)}=\frac{sqrt{3}}{2}\),更一般地说、对于 d 维欧几里得空间,(\frac{1}{sqrt{d}}le \beta ^*_{({\mathbb {R}}^d,\Vert \cdot \Vert _2)}le \frac{sqrt{3}}{2})。在本文中,我们证明了对于任意维度 d(注意,对于任意维度 d(d\ge 4\),\(\frac{1}{sqrt{d}<0.557\le \beta ^*_{({\mathbb {R}}^d,\Vert \cdot \Vert _2)})。此外,我们还证明了对于每个度量空间(X,d)来说,\(\sqrt{2}-1\le \beta ^*_{(X,d)}\) 都是成立的,并且证明了存在一个度量空间,对于这个空间来说,\(\beta ^*_{(X,d)}le \frac{1}{2}\) 是存在的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Plurality in Spatial Voting Games with Constant $$\beta $$

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Plurality in Spatial Voting Games with Constant $$\beta $$

Consider a set V of voters, represented by a multiset in a metric space (Xd). The voters have to reach a decision—a point in X. A choice \(p\in X\) is called a \(\beta \)-plurality point for V, if for any other choice \(q\in X\) it holds that \(|\{v\in V\mid \beta \cdot d(p,v)\le d(q,v)\}| \ge \frac{|V|}{2}\). In other words, at least half of the voters “prefer” p over q, when an extra factor of \(\beta \) is taken in favor of p. For \(\beta =1\), this is equivalent to Condorcet winner, which rarely exists. The concept of \(\beta \)-plurality was suggested by Aronov, de Berg, Gudmundsson, and Horton [TALG 2021] as a relaxation of the Condorcet criterion. Let \(\beta ^*_{(X,d)}=\sup \{\beta \mid \text{ every } \text{ finite } \text{ multiset } V{ in}X{ admitsa}\beta \text{-plurality } \text{ point }\}\). The parameter \(\beta ^*\) determines the amount of relaxation required in order to reach a stable decision. Aronov et al. showed that for the Euclidean plane \(\beta ^*_{({\mathbb {R}}^2,\Vert \cdot \Vert _2)}=\frac{\sqrt{3}}{2}\), and more generally, for d-dimensional Euclidean space, \(\frac{1}{\sqrt{d}}\le \beta ^*_{({\mathbb {R}}^d,\Vert \cdot \Vert _2)}\le \frac{\sqrt{3}}{2}\). In this paper, we show that \(0.557\le \beta ^*_{({\mathbb {R}}^d,\Vert \cdot \Vert _2)}\) for any dimension d (notice that \(\frac{1}{\sqrt{d}}<0.557\) for any \(d\ge 4\)). In addition, we prove that for every metric space (Xd) it holds that \(\sqrt{2}-1\le \beta ^*_{(X,d)}\), and show that there exists a metric space for which \(\beta ^*_{(X,d)}\le \frac{1}{2}\).

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