Collaborative Research: Development of a Technology for Real Time Ex Ante Forecasting of Intra and International Conflict and Cooperation

合作研究：开发实时事前预测内部和国际冲突与合作的技术

• 批准号: 0921018
• 负责人: John Freeman
• 金额: $ 16.84万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0921018&HistoricalAwards=false
• 关键词: Collaborative; Research; Development; Technology; Real

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Conflicts continue to rage in the Levant, South Asia, and elsewhere. Governmental, nongovernmental, and intergovernmental organizations are seeking a technology that will help them anticipate the outbreak of violence. Some progress has been made predicting single events and political instability two years ahead. But we lack scientifically sound tools for gauging the statistical uncertainty of these and other forecasts, making predictions in real-time, analyzing the behavior of groups of belligerents, and making contingent forecasts (forecasts conditional on possible interventions by third parties). We develop a scientifically advanced technology that meets these desiderata: a tool that produces on an open source website in real-time, ex ante forecasts of intra- and international conflict and cooperation for three parts of the world. The time series data is produced by state-of-the art TABARI automated coding software using the CAMEO event and actor coding system; the source are news feed aggregators such as Google News and the European Monitor accessed via real simple syndication feeds (RSS). Bayesian multivariate time series models with Markov-switching are fit to these data. These models then produce forecast probability densities for systems of directed dyadic behaviors. Model performance is based on density evaluation; model refinements are made via developed recalibration methods. The website will report forecasts for selected systems of belligerents in the Levant, South Asia, and East Asia; it will disseminate the coded event data, real-time forecasts of these cases with measures of forecast uncertainty, contingent forecasts based on possible interventions, information on model performance. The technology will meet most, if not all, of the desiderata for forecasting in international relations. Three specific contributions are be made. First, the feasibility and value of automated real-time events coding is demonstrated. Until now, this coding has lagged real-time; and the value of the data has not been realized. The application of current statistical work on forecasting is a second contribution. For the first time in political science we produce and evaluate density (not point) forecasts. And we refine our forecasting models with newly developed recalibration techniques. Third, the well known tendency for conflict and cooperation phase shifts are explicitly analyzed and incorporated in our forecasts. The Markov-switching element of our model capture these nonlinearities. Using the Teragrid we produce probability weighted forecasts of behavioral systems occupying certain states, report transition probabilities, and include estimates of the uncertainty of both. Thus the final forecasting models will be original in international relations. The forecasts we produce will be available for use by NGOs, IGOs, and any party interested in anticipating conflict in the Levant, South Asia, and East Asia. To help these parties use our forecasts we will make presentations at meetings held by the A.P.S.A, I.S.A, and I.P.S.A. We also will present our work and publicize our website in countries like Taiwan via institutions like the Academia Sinica. Eventually, our website will report contingent forecasts based on inputs from scholars and policy makers' forecasts of what will transpire in the short-term if certain kinds of intervention occur. Estimates of the probability of phase shifts and of forecast densities will accompany these contingent forecasts.

该奖项是根据2009年的《美国复苏与重新投资法》（公法111-5）资助的。联合会在黎凡特，南亚和其他地方继续愤怒。政府，非政府和政府间组织正在寻求一项技术，以帮助他们预期暴力爆发。已经取得了一些进展，预测了两年的单一事件和政治动荡。但是，我们缺乏科学的声音工具来衡量这些和其他预测的统计不确定性，实时做出预测，分析交战群体的行为，并进行或进行或进行预测（预测是根据第三方可能的干预措施有条件的）。我们开发了一项科学先进的技术，该技术符合这些Desiderata：一种实时在开源网站上生产的工具，对世界三个地区的内部和国际冲突和合作进行了预测。时间序列数据是由最先进的Tabari自动编码软件使用客串事件和演员编码系统生成的；来源是新闻供稿聚合商，例如Google News和通过真实的简单联合提要（RSS）访问的欧洲监视器。带有马尔可夫开关的贝叶斯多元时间序列模型适合这些数据。然后，这些模型为定向二元行为系统产生预测概率密度。模型性能基于密度评估；模型改进是通过开发的重新校准方法进行的。该网站将报告对黎凡特，南亚和东亚选定的交战系统的预测；它将以预测不确定性的衡量标准，基于可能的干预措施以及有关模型性能的信息来传播编码事件数据，对这些情况的实时预测。该技术将达到国际关系中预测的最多（如果不是全部）的义务。做出了三个具体的贡献。首先，展示了自动实时事件编码的可行性和价值。到目前为止，该编码已经实时滞后。数据的价值尚未实现。当前统计工作在预测上的应用是第二个贡献。在政治学中，我们第一次生产和评估密度（不是点）的预测。我们通过新开发的重新校准技术完善了预测模型。第三，对冲突和合作阶段转移的众所周知的趋势被明确分析并纳入了我们的预测中。我们模型的马尔可夫开关元素捕获了这些非线性。使用TeraGRID，我们产生了占据某些状态，报告过渡概率的行为系统的概率加权预测，并包括两者的不确定性的估计。因此，最终的预测模型将在国际关系中是原始的。我们产生的预测将由非政府组织，IGO和有兴趣预期黎凡特，南亚和东亚冲突的任何一方使用。为了帮助这些各方使用我们的预测，我们将在A.P.S.A，I.S.A和I.P.S.A.举行的会议上进行演讲。我们还将通过Sinica等机构在台湾等国家 /地区宣传我们的工作，并在台湾等国家 /地区宣传我们的网站。最终，我们的网站将根据学者和政策制定者对如果发生某些干预措施在短期内会爆发的预测的投入来报告应有的预测。这些应急预测的估计值和预测密度的概率和预测密度的估计值。