Forecasting is difficult, especially about the future
Forecasting is difficult, especially about the future
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预测很困难，尤其是未来
DOI：
10.1177/0022343312449033
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发表时间：
2013
影响因子：
3.6
通讯作者：
M. Ward
中科院分区：
文献类型：
--
作者：
K. Gleditsch;M. Ward
预测是国际冲突研究的一个重要目标，但大量的研究发现，现有的统计模型的预测能力普遍令人失望。我们发现，大多数努力建立在模型上，不太可能有助于预测。许多模式基本上忽略了冲突的根源;研究要么着眼于据信影响冲突机会的不变结构特征，要么着眼于据信减少冲突基线风险的因素，而不试图确定通常引起冲突的潜在动机和有争议的问题。研究人员已经考虑了有争议的问题如何激发冲突以及如何使用战争问题相关性（ICOW）数据来管理这些问题，但没有考虑这些特征如何为预测提供信息。我们评估的基础上存在的具体有争议的问题和冲突管理事件，可能会改变这些有争议的问题的冲突潜力的二元国家间冲突的风险。我们评估在多大程度上纳入有争议的问题和冲突管理可以帮助改善样本预测，以及推进我们对冲突动态的理解。我们的研究结果提供了强有力的支持，考虑到有争议的问题，可以通知和改善样本外预测的想法。
Prediction is an important goal in the study of international conflict, but a large body of research has found that existing statistical models generally have disappointing predictive abilities. We show that most efforts build on models unlikely to be helpful for prediction. Many models essentially ignore the origins of conflict; studies look either at invariant structural features believed to affect the opportunities of conflict, or at factors that are believed to reduce the baseline risk of conflict, without attempting to identify the potential motivations and contentious issues over which conflicts typically arise. Researchers that have considered how contentious issues may motivate conflict and how these can be managed, using the Issues Correlates of War (ICOW) data, have not considered how these features may inform prediction. We assess the risk of dyadic interstate conflict based on the presence of specific contentious issues and conflict management events that may change the conflict potential of these contentious issues. We evaluate to what extent incorporating contentious issues and conflict management can help improve out-of-sample forecasting, as well as advance our understanding of conflict dynamics. Our results provide strong support for the idea that taking into account contentious issues can inform and improve out-of-sample forecasting.