Abstract

This article illustrates an approach to forecasting change in conflict fatalities designed to address the complexity of the drivers and processes of armed conflicts. The design of this approach is based on two main choices. First, to account for the specificity of conflict drivers and processes over time and space, we model conflicts in each individual country separately. Second, we draw on an adaptive model—Dynamic Elastic Net, DynENet—which is able to efficiently select relevant predictors among a large set of covariates. We include over 700 variables in our models, adding event data on top of the data features provided by the convenors of the forecasting competition. We show that our approach is suitable and computationally efficient enough to address the complexity of conflict dynamics. Moreover, the adaptive nature of our model brings a significant added value. Because for each country our model only selects the variables that are relevant to predict conflict intensity, the retained predictors can be analyzed to describe the dynamic configuration of conflict drivers both across countries and within countries over time. Countries can then be clustered to observe the emergence of broader patterns related to correlates of conflict. In this sense, our approach produces interpretable forecasts, addressing one key limitation of contemporary approaches to forecasting.

摘要

本文阐述了一种预测冲突死亡人数变化的方法，旨在解决武装冲突驱动因素和过程的复杂性。这种方法的设计基于两个主要选择。首先，为了说明冲突驱动因素和过程在时间和空间上的特殊性，我们分别对每个国家的冲突进行建模。其次，我们利用了一个自适应模型——Dynamic Elastic Net，DynENet——它能够在大量协变量中有效地选择相关的预测变量。我们在模型中包含 700 多个变量，在预测竞赛召集人提供的数据特征之上添加事件数据。我们表明，我们的方法适用于且计算效率足以解决冲突动态的复杂性。而且，我们模型的适应性带来了显着的附加值。因为对于每个国家，我们的模型只选择与预测冲突强度相关的变量，因此可以分析保留的预测变量来描述不同国家和国家内部的冲突驱动因素随时间推移的动态配置。然后可以对国家进行聚类，以观察与冲突相关性相关的更广泛模式的出现。从这个意义上说，我们的方法产生了可解释的预测，解决了当代预测方法的一个关键限制。然后可以对国家进行聚类，以观察与冲突相关性相关的更广泛模式的出现。从这个意义上说，我们的方法产生了可解释的预测，解决了当代预测方法的一个关键限制。然后可以对国家进行聚类，以观察与冲突相关性相关的更广泛模式的出现。从这个意义上说，我们的方法产生了可解释的预测，解决了当代预测方法的一个关键限制。