Longitudinal Network Modeling of International Relations Data

对国际关系数据的实证分析已成为研究人员评估贸易、冲突和国家间其他相互作用理论的主要方法之一。 虽然这些分析中使用的数据是纵向的，反映了网络模式，涉及国家之间的长期关系，但这些统计建模工作很少考虑数据的时间性质，更常见的是，网络效应被忽视。 此外，很少有人试图通过与正在发生的事件进行比较来衡量所获得的模型拟合结果的有效性，尽管在以科学为导向的国际政治研究中，许多核心方法都源于强烈的政策关注。 为了解决这些问题，该项目将开发和实施关系数据统计模型，其中考虑到（a）国际关系随时间的演变，以及（B）关系、社会网络数据固有的复杂依赖关系。 这将通过将网络数据的回归和潜在因素模型扩展到时间域来实现，允许使用社会科学研究人员熟悉的工具分析复杂的纵向关系数据。虽然网络在国际事务中很重要，但网络效应及其动态往往被忽视。 本项目所制定的方法将改进评价国际关系的标准方法，说明基本网络模式以及观测数据的动态特征。 采用这种方法，就有可能准确地识别和区分国际关系中的稳定网络效应和动态网络效应，从而更全面地了解国家间政治和经济关系的演变。 该奖项是作为2006财政年度数学科学优先领域数学社会和行为科学（MSBS）特别竞赛的一部分获得支持的。

Empirical analyses of international relations data have become one of the principal methods by which researchers evaluate theories of trade, conflict, and other interactions among countries. Although the data used in these analyses are longitudinal and reflect network patterns, involving measured relations among nations over time, very seldom is the temporal nature of the data accounted for in these statistical modeling efforts, and even more frequently, the network effects are ignored. Furthermore, rarely is there any attempt to gauge the validity of the obtained model-fitting results by comparison to unfolding events, despite the fact that many of the core approaches in scientifically oriented studies of international politics spring from strong policy concerns. To address these issues, the project will develop and implement statistical models for relational data that take into account both (a) the evolution of international relations over time, and (b) the complex dependencies inherent to relational, social network data. This will be done by extending regression and latent factor models for network data to the time domain, allowing for the analysis of complicated longitudinal relational data using tools that are familiar to social science researchers.Although networks are important in international affairs, network effects and their dynamics are often ignored. The methodology developed in this project will improve upon the standard methods for evaluating international relations by accounting for underlying network patterns as well as the dynamic character of the observed data. With such an approach, it will be possible to accurately identify and distinguish stable from dynamic network effects in international relations and thereby provide a more complete picture of the evolution of the political and economic relationships among nations. This award was supported as part of the fiscal year 2006 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).