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.

中文翻译：

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一种测量状态偏好的网络方法

国家偏好在国际政治中发挥着重要作用。不幸的是，实际上观察和衡量这些偏好是不可能的。一般来说，学者们试图通过联合国投票或联盟行为来推断偏好。来自该研究领域的两个最显着的国家偏好衡量标准是理想点 (Bailey et al., 2017) 和 S-scores (Signorino & Ritter, 1999)。这两种模型的基础是一种空间加权方案，该方案已被证明是有用的，但忽略了联合国投票和联盟等关系数据结构中可能存在的高阶效应。我们首先争辩说，联盟和联合国投票都只是国家相互影响的多层的例子。为了估计状态偏好的度量，我们利用张量分解模型提供跨层主要模式的降阶近似。我们对偏好的新衡量标准似是而非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见解。此外，我们表明，在预测样本外环境中的冲突时，使用这种状态偏好度量的冲突模型明显优于使用现有度量的模型。