The Mitchell Centre for Social Network Analysis, The University of Manchester, Manchester, M13 9PL, UK.
Social Networks Lab, Department of Humanities, Social and Political Sciences, ETH Zürich, Zürich, Switzerland.
Sci Rep. 2020 Oct 15;10(1):17369. doi: 10.1038/s41598-020-74175-w.
Scaling techniques such as the well known NOMINATE position political actors in a low dimensional space to represent the similarity or dissimilarity of their political orientation based on roll-call voting patterns. Starting from the same kind of data we propose an alternative, discrete, representation that replaces positions (points and distances) with niches (boxes and overlap). In the one-dimensional case, this corresponds to replacing the left-to-right ordering of points on the real line with an interval order. As it turns out, this seemingly simplistic one-dimensional model is sufficient to represent the similarity of roll-call votes by U.S. senators in recent years. In a historic context, however, low dimensionality represents the exception which stands in contrast to what is suggested by scaling techniques.
我们提出了一种替代的、离散的表示方法，用生态位（盒子和重叠）代替位置（点和距离）。在一维情况下，这对应于用区间序代替实线上点的从左到右的顺序。事实证明，这种看似简单的一维模型足以代表近年来美国参议员的投票相似度。然而，从历史的角度来看，低维性是一种例外，与标度技术所暗示的相反。
