论文标题
通过动态弹性网的预测发生冲突死亡的变化
Forecasting Change in Conflict Fatalities with Dynamic Elastic Net
论文作者
论文摘要
本文说明了一种预测冲突死亡变化的方法，旨在解决武装冲突的驾驶员和过程的复杂性。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流程随时间和空间的特异性，我们分别对每个国家的冲突进行建模。其次，我们利用自适应模型 - 动态弹性网，Dynenet-能够在大量协变量中有效选择相关的预测因子。我们在模型中包括了700多个变量，在预测竞争的竞争者提供的数据功能之上添加了事件数据。我们表明，我们的方法是合适的，并且有效地有效地解决了冲突动态的复杂性。此外，我们模型的适应性具有显着的附加值。因为对于每个国家 /地区，我们的模型仅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因子，以描述各个国家和国家内部随着时间的推移的冲突驱动因素的动态配置。然后可以聚集国家以观察与冲突相关的更广泛模式的出现。从这个意义上讲，我们的APRach产生了可解释的预测，解决了当代AP-Haperes对预测的一个关键局限性。
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 con-venors of the forecasting competition. We show that our approach is suitable and computa-tionally 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 ap-proach produces interpretable forecasts, addressing one key limitation of contemporary ap-proaches to forecasting.
