Kushwaha Niraj, Lee Edward D
Complexity Science Hub, Josefstædter Strasse 39, 1080 Vienna, Austria.
PNAS Nexus. 2023 Aug 1;2(7):pgad228. doi: 10.1093/pnasnexus/pgad228. eCollection 2023 Jul.
Conflicts, like many social processes, are related events that span multiple scales in time, from the instantaneous to multi-year development, and in space, from one neighborhood to continents. Yet, there is little systematic work on connecting the multiple scales, formal treatment of causality between events, and measures of uncertainty for how events are related to one another. We develop a method for extracting causally related chains of events that addresses these limitations with armed conflict. Our method explicitly accounts for an adjustable spatial and temporal scale of interaction for clustering individual events from a detailed data set, the Armed Conflict Event & Location Data Project. With it, we discover a mesoscale ranging from a week to a few months and tens to hundreds of kilometers, where long-range correlations and nontrivial dynamics relating conflict events emerge. Importantly, clusters in the mesoscale, while extracted from conflict statistics, are identifiable with mechanism cited in field studies. We leverage our technique to identify zones of causal interaction around conflict hotspots that naturally incorporate uncertainties. Thus, we show how a systematic, data-driven, and scalable procedure extracts social objects for study, providing a scope for scrutinizing and predicting conflict and other processes.
冲突与许多社会进程一样，是跨越多个时间尺度（从瞬间到多年发展）和空间尺度（从一个街区到各大洲）的相关事件。然而，在连接多个尺度、对事件之间的因果关系进行形式化处理以及衡量事件之间相互关系的不确定性方面，几乎没有系统性的工作。我们开发了一种方法，用于提取与武装冲突相关的因果事件链，以解决这些局限性。我们的方法明确考虑了可调整的时空交互尺度，以便从详细数据集“武装冲突事件与地点数据项目”中对单个事件进行聚类。通过该方法，我们发现了一个从一周到几个月、几十到数百公里的中尺度范围，在这个范围内出现了与冲突事件相关的长程相关性和非平凡动力学。重要的是，中尺度的聚类虽然是从冲突统计数据中提取的，但可以通过实地研究中引用的机制来识别。我们利用我们的技术来识别冲突热点周围自然包含不确定性的因果交互区域。因此，我们展示了一个系统的、数据驱动的且可扩展的程序如何提取用于研究的社会对象，为审视和预测冲突及其他进程提供了空间。
