A network approach to measuring state preferences
A network approach to measuring state preferences
复制标题
衡量国家偏好的网络方法
作者：
Max Gallop;Shahryar Minhas
摘要国家偏好在国际政治中起着重要作用。不幸的是，实际观察和测量这些偏好是不可能的。一般来说，学者们都试图通过联合国投票或联盟行为来推断偏好。从这一研究领域流出的两个最值得注意的国家偏好度量是理想点（Bailey等人，2017）和S分数（Signorino & Ritter，1999）。这两个模型的基础是一个空间加权方案，已被证明是有用的，但折扣高阶效应，可能存在于关系数据结构，如联合国投票和联盟。我们开始认为，联盟和联合国投票都只是国家相互作用的多层次的例子。为了估计状态偏好的度量，我们利用张量分解模型，该模型提供跨层的主要模式的降秩近似。我们的新偏好度量合理地描述了重要的国家关系，并对偏好、民主和国际冲突之间的关系产生了重要的见解。此外，我们表明，使用这种措施的状态偏好的冲突模型决定性地优于模型使用现有的措施，当涉及到预测冲突的样本外的上下文中。
Abstract State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al., 2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context.