Civil conflict appears to be contagious—scholars have shown that civil wars in a state’s neighborhood make citizens more likely to rebel at home. However, war occurs when both rebels and the state engage in conflict. How do state authorities respond to the potential for civil conflict to spread? We argue that elites will anticipate the incentive-altering effects of civil wars abroad and increase repression at home to preempt potential rebellion. Using a Bayesian hierarchical model and spatially weighted conflict measures, we find robust evidence that a state will engage in higher levels of human rights violations as civil war becomes more prevalent in its geographic proximity. We thus find evidence that states violate rights as a function of the internal politics of other states. Further, we argue authorities will act not to mimic their neighbors but rather to avoid their fate.

Empirical political science is not simply about reporting evidence; it is also about coming to conclusions on the basis of that evidence and acting on those conclusions. But whether a result is substantively significant-strong and certain enough to justify acting upon the belief that the null hypothesis is false-is difficult to objectively pin down, in part because different researchers have different standards for interpreting evidence. Instead, we advocate judging results according to their "substantive robustness," the degree to which a community with heterogeneous standards for interpreting evidence would agree that the result is substantively significant. We illustrate how this can be done using Bayesian statistical decision techniques. Judging results in this way yields a tangible benefit: false positives are reduced without decreasing the power of the test, decreasing the error rate in published results. Introduction: statistical inference and rational choice under uncertaintyA long and and cross-disciplinary literature stresses the importance of assessing the substantive significance of empirical results (Achen

Scholars of international conflict mediation have made strides in the last two decades in understanding when mediation occurs and when it is successful. The rationalist framework has allowed theorists to sharpen and expand on early insights, and research using quantitative methods continues to be an important part of the field. Gaining a sense of when disputants might use mediation disingenuously and expanding the scope and comparability of data sets on mediation will push both the study and practice of mediation onto useful new ground.

No abstract

We present a machine learning approach to improve the accuracy of summarized incident report visualizations for cyber security. We extend a recent incident report summarization method by training a Bayesian hierarchical model to optimize the summarization algorithm's weights. We also train a flat model and a neural network as alternative models to compare against our hierarchical model. Summaries generated by our hierarchical model achieve higher accuracy than the other methods, with an AUC 0.2 higher than the unweighted method while achieving comparable summarization size. We further demonstrate that visualizations of the hierarchical model's summaries are at least as useful the unweighted method's summaries, and possibly more useful.