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Temporal Analysis of Political Instability through Descriptive Subgroup Discovery

Author

Listed:
  • Daniel Lambach

    (Institute for Development and Peace University of Duisburg-Essen Duisburg, Germany, daniel.lambach@inef.uni-due.de)

  • Dragan Gamberger

    (Laboratory for Information Systems Rudjer Boskovic Institute Zagreb, Croatia)

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.

Suggested Citation

  • Daniel Lambach & Dragan Gamberger, 2008. "Temporal Analysis of Political Instability through Descriptive Subgroup Discovery," Conflict Management and Peace Science, Peace Science Society (International), vol. 25(1), pages 19-32, February.
    • Handle: RePEc:sae:compsc:v:25:y:2008:i:1:p:19-32
    DOI: 10.1080/07388940701860359
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    References listed on IDEAS

    as
    1. Sartori, Giovanni, 1970. "Concept Misformation in Comparative Politics," American Political Science Review, Cambridge University Press, vol. 64(4), pages 1033-1053, December.
    2. Fearon, James D. & Laitin, David D., 2003. "Ethnicity, Insurgency, and Civil War," American Political Science Review, Cambridge University Press, vol. 97(1), pages 75-90, February.
    3. James D. Fearon, 2004. "Why Do Some Civil Wars Last So Much Longer than Others?," Journal of Peace Research, Peace Research Institute Oslo, vol. 41(3), pages 275-301, May.
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