Formal models of political behavior have generally followed the lead of the natural sciences and focused on methods that use continuous-variable mathematics. Stephen Wolfram has recently produced an extended critique of that approach in the natural sciences, and suggested that a great deal of natural behavior can be accounted for using rules that involve discrete patterns. Given the similarity between many of the models used in the natural and social sciences, Wolfram's critique can readily be applied to models of social behavior. Pattern-based models are especially relevant to modeling human behavior because human cognitive abilities are far more developed in the domain of pattern recognition than in the domain of continuous-variable mathematics. International event data---categorical data on who did what to whom at what time---are one of the most common forms of information available for the analysis of international behavior. Contemporary automated methods combined with inexpensive full-text news sources such as FBIS, NEXIS, and Factiva allow event data to be generated in near-real-time with relatively little effort. For example, it is now possible for a graduate student to generate a customized data set containing tens of thousands of events as part of a dissertation. However, consistent with Wolfram's critique, the methods for analyzing, or even visualizing, event data have generally not kept up with the advances in the generation of the data, largely because categorical time series have no analogue in other quantitatively-oriented social sciences such as economics and demography. In these circumstances, the nearly universal tendency has been to convert the data to interval-level measures using scales prior to analyzing it. This has at least three disadvantages: first, the scales are somewhat arbitrary; second information is lost when multiple events .cancel out. and third, most psychological evidence indicates that decision-makers respond to patterns of discrete events rather than numerical aggregations. This project develops and test several pattern- and rule-based analytical tools for the analysis of event data. These involve the development of visualization methods, methods for specifying patterns and rules at various levels of complexity, methods for statistically assessing and comparing patterns and rules, and methods for rule induction. These tools will be deployed on a publicly-accessible dedicated web server. That site will also provide a large number of event data sets in an easily accessible form with basic downloading and subsetting tools. All of the software developed on the project will be open-source under the GNU General Public License; the event data sets and analytical tools will be made available as they are produced rather than being embargoed. The project will experiment with patterns applied to the Israel-Palestinian and Israel-Lebanon conflicts for 1979-2005 and the conflict in the former Yugoslavia for 1991-2001. It will start with the classic tit-for-tat pattern, consider patterns designed to detect attempts at de-escalation, and finally consider complex "meta-rules" that look at the relationship between prior conflict and the propensity of the actors to engage in reciprocal behavior. The principal investigators anticipate conducting half-day workshops at the APSA and ISA to introduce researchers to the use of these tools. Broader Impacts: First, it will utilize undergraduate students, typically honors students in international studies, extensively in the research effort. Both Kansas and Utah are geographical regions under-represented in scientific research so these opportunities are particularly critical. Second, the project will produce new data and analytical tools that can be used by the research community. Third, this approach is likely to be very attractive for the policy community and may be used for forecasting and other policy-relevant tasks.
