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.
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