Predicting political violence using a state-space model
Predicting political violence using a state-space model
复制标题
使用状态空间模型预测政治暴力
DOI：
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发表时间：
2022
影响因子：
1.3
通讯作者：
Thomas Bo Schön
中科院分区：
文献类型：
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作者：
A. Lindholm;J. Hendriks;A. Wills;Thomas Bo Schön
摘要：我们提供了一种新的状态空间建模方法，用于预测每月因政治暴力死亡的概念验证。注意力集中在开发的方法，并展示这种方法的实用性，这提供了令人兴奋的机会，从事领域专家在开发新的和改进的状态空间模型预测暴力。预测是在空间分辨率为0.5 × 0.5度的网格单元上进行的，每个单元都被建模为具有两个数学上定义良好的未观察/潜伏/隐藏状态，这些状态随着时间的推移而演变，并分别编码“发病风险”和“潜在严重程度”。这为模型提供了一定程度的可解释性。通过使用该模型来计算以直到当前时间为止观察到的所有数据为条件的未来时间的死亡计数的概率分布，获得预测分布。预测分布通常在死亡计数0（没有暴力爆发）处放置一定的质量，剩余的质量指示死亡计数的可能间隔，如果暴力爆发出现。为了评估模型性能，我们（缺乏更好的替代方案）报告了预测分布的平均值，但对预测分布的访问本身就是对应用程序的一个有趣的贡献。这项工作只是作为一个概念验证的状态空间建模方法，这种类型的数据和几个可能的方向，进一步的工作，可以提高预测性能的建议。
Abstract We provide a proof-of-concept for a novel state-space modelling approach for predicting monthly deaths due to political violence. Attention is focused on developing the method and demonstrating the utility of this approach, which provides exciting opportunities to engage with domain experts in developing new and improved state-space models for predicting violence. The prediction is made on a grid of cells with spatial resolution of 0.5 × 0.5 degrees, and each cell is modeled to have two mathematically well-defined unobserved/latent/hidden states that evolves over time and encode the “onset risk” and “potential severity”, respectively. This offers a certain level of interpretability of the model. By using the model for computing the probability distribution for a death count at a future time conditioned on all data observed up until the current time, a predictive distribution is obtained. The predictive distribution typically places a certain mass at the death count 0 (no violent outbreak) and the remaining mass indicating a likely interval of the fatality count, should a violent outbreak appear. To evaluate the model performance we—lacking a better alternative—report the mean of the predictive distribution, but the access to the predictive distribution is in itself an interesting contribution to the application. This work merely serves as a proof-of-concept for the state-space modeling approach for this type of data and several possible directions for further work that could improve the predictive performance are suggested.