Temporal analysis of political instability through descriptive subgroup discovery

Abstract

This paper analyzes the Political Instability Task Force (PITF) data set using a new methodology based on machine learning tools for subgroup discovery. While the PITF used static data, this study employs both static and dynamic descriptors covering the 5-year period before onset. The methodology provides several descriptive models of countries especially prone to political instability. For the most part, these models corroborate the PITF’s findings and support earlier theoretical works. The paper also shows the value of subgroup discovery as a tool for developing a unified concept of political instability as well as for similar research designs.