Short Term Prediction of International Events Using Pattern Recognition

这个项目的重点是开发人工智能方法，以发现国际行为的短期模式。当代对国际政治的大部分研究都聚焦于“事件”：民族国家之间的离散互动。事件可能像战争爆发或签署重大贸易协定一样戏剧性，也可能像一个政府祝贺另一个政府独立日一样简单。可以将单个事件分组为描述更复杂行为的事件序列，例如冲突、谈判或关系变化。对国际行为的人类观察者，如记者、历史学家和政治学家，都知道国际事件是有规律的。在战争之前，紧张局势几乎不可避免地会升级；在达成贸易协议之前，将会进行数月的谈判。这些模式在国际事务中提供了足够的规律性，以至于专家分析师通常对下一步可能发生的事件有很好的了解，还可以发现行为的异常变化。该项目将使用人工智能开发的模式识别方法来检测中东当代事件的短期规律性。这些活动将根据美国政府印刷局提供的外国广播信息服务(FBIS)报告进行编码。联邦调查局每天报告数百起事件：这些事件将被编码成标准的事件类别，然后计算机程序将在这些事件数据中寻找重复模式。然后，这些模式就可以用来进行预测，就像人类分析师利用观察到的国际行为规律进行预测一样。该项目使用了两种人工智能方法。一种方法是构造“部分有序事件序列”--重复观察的事件序列，因为某些事件必须在其他事件之前发生(例如，必须先进行谈判才能达成贸易协定)。另一种方法是使用“遗传算法”，通过类似于进化的过程，从简单的序列中组合出复杂的事件序列。首席调查员已经发表了几篇使用这些方法处理历史数据的论文；该项目将是第一个使用这些方法处理当代数据的项目。该项目将为理解国际行为做出三项贡献。首先，这将是创建当代国际体系--中东--日常行为的计算机模型的首批努力之一。虽然国际关系理论为国际政治的长期趋势提供了许多计算机模型，但对短期行为的研究相对较少。其次，该项目将创建用于发现政治行为模式的通用工具。虽然这些技术正在被开发来研究国际行为，但同样的方法也可以用来研究任何其他可以用事件来描述的政治、经济或社会行为。最后，该项目将提供一些新工具，利用相对便宜的微型计算机设备处理每天产生的大量数据。

This project focuses on the development of artificial intelligence methods for finding short-term patterns in international behavior. Much of the contemporary study of international politics focuses on "events": discrete interactions between nation-states. Events may be as dramatic as the outbreak of war or the signing of a major trade agreement, or as simple as one government congratulating another on its independence day. Individual events can be group into event sequences which describe more complicated behaviors such as conflicts, negotiations or changes in relations. Human observers of international behavior such as journalists, historians and political scientists know that international events follow patterns. A war will almost inevitably be preceded by escalating tensions; a trade agreement will be preceded by months of negotiations. These patterns provide enough regularity in international affairs that expert analysts usually have a good idea of what the likely next events will be, and can also detect unusual changes in behavior. This project will use pattern recognition methods developed in artificial intelligence to detect short-term regularities in contemporary events in the Middle East. The events will be coded from the Foreign Broadcast Information Service (FBIS) reports available from the U.S. Government Printing Office. FBIS reports several hundred events per day: these will be coded into standard categories of events, then the computer programs will look for repeated patterns in those events data. Those patterns can then be used to make predictions much as human analysts use the observed regularities in international behavior to make predictions. The project uses two artificial intelligence methods. One method constructs "partially-ordered event sequences" - - sequences of events which are observed repeatedly because certain events must be preceded by other events (for example, one must have negotiations before one can have a trade agreement). The other method uses "genetic algorithms", which assemble complex event sequences out of simpler sequences using a process resembling evolution. The Principal Investigator has already published several papers employing these methods on historical data; this project will be the first to use them on contemporary data. The project will make three contributions to the understanding of international behavior. First, it will be one of the first efforts to create a computer model of the day-to-day behavior of a contemporary international system, the Middle East. While international relations theory has provided a number of computer models for long-term trends in international politics, relatively little work has been done on short- term behavior. Second, the project will create general tools for finding patterns in political behavior. While these techniques are being developed to study international behavior, the same methods could be used to study any other political, economic or social behavior which can be described using events. Finally, the project will provide some new tools for dealing with large amounts of data generated on a daily basis using relatively inexpensive microcomputer equipment.