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Dynamics of International Mediation: Analysis Using Machine Learning Methods

Author

Listed:
  • Anne-Katrin Wickboldt

    (Massachusetts Institute of Technology)

  • Jacob Bercovitch

    (University of Canterbury, Christchurch, New Zealand)

  • Selwyn Piramuthu

    (University of Pennsylvania)

Abstract

This paper develops a framework to help us understand the dynamics of international mediation efforts and their consequences. This approach identifies the relevant variables that influence the success of these mediation efforts as well as the relationships among these variables in influencing mediation outcomes. The framework incorporates techniques that have been developed under the rubric of machine learning, specifically feature selection and induced decision trees. In addition to confirming some results from previous studies, results from this study provide new insights on some of the most important factors affecting international mediation.

Suggested Citation

  • Anne-Katrin Wickboldt & Jacob Bercovitch & Selwyn Piramuthu, 1999. "Dynamics of International Mediation: Analysis Using Machine Learning Methods," Conflict Management and Peace Science, Peace Science Society (International), vol. 17(1), pages 49-68, February.
    • Handle: RePEc:sae:compsc:v:17:y:1999:i:1:p:49-68
    DOI: 10.1177/073889429901700102
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    References listed on IDEAS

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    1. Dixon, William J., 1996. "Third-party techniques for preventing conflict escalation and promoting peaceful settlement," International Organization, Cambridge University Press, vol. 50(4), pages 653-681, October.
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    Cited by:

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    3. Esteban Alfaro Cortés & Matías Gámez Martínez & Noelia García Rubio, 2007. "Multiclass Corporate Failure Prediction by Adaboost.M1," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 301-312, August.

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