School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom.
Proc Natl Acad Sci U S A. 2012 Jul 31;109(31):12414-9. doi: 10.1073/pnas.1203177109. Epub 2012 Jul 16.
Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. Using ideas from statistics, signal processing, and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the WikiLeaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from previous years.
现代冲突的特点是越来越多地使用信息和传感技术，从而产生了大量高分辨率的数据。然而，由于通常可用的数据具有异构性和动态性，因此对冲突进行建模和预测仍然是具有挑战性的任务。在这里，我们提出使用动态时空建模工具来识别冲突中复杂的潜在过程，例如扩散、迁移、异质升级和波动性。我们借鉴统计学、信号处理和生态学的思想，提供了一个能够同化数据并对预测进行置信度估计的预测框架。我们在 WikiLeaks 阿富汗战争日记上展示了我们的方法。我们的结果表明，该方法允许更深入地了解冲突动态，并允许仅基于前几年的数据，对 2010 年武装反对派活动进行惊人的统计学上准确的向前预测。
