Replication data for: Improving Quantitative Studies of International Conflict: A Conjecture

We address a well-known but infrequently discussed problem in the quantitative study of international conflict: Despite immense data collections, prestigious journals, and sophisticated analyses, empirical findings in the literature on international conflict are often unsatisfying. Many statistical results change from article to article and specification to specification. Accurate forecasts are nonexistent. In this article we offer a conjecture about one source of this problem: The causes of conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large, stable, and replicable wherever the ex ante probability of conflict is large. This simple idea has an unexpectedly rich array of observable implications, all consistent with the literature. We directly test our conjecture by formulating a statistical model that include s critical features. Our approach, a version of a \"neural network\" model, uncovers some interesting structural features of international conflict, and as one evaluative measure, forecasts substantially better than any previous effort. Moreover, this improvement comes at little cost, and it is easy to evaluate whether the model is a statistical improvement over the simpler models commonly used. Winner of the Gosnell Prize. See also our response to a published comment on this paper: Beck, Nathaniel; Gary King; and Langche Zeng, \"Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski,\" American Political Science Review, 98, 2 (May, 2004): 379--389 (Article: PDF | Abstract: HTML< /a>), and a related paper King, Gary; Zeng, Langche, \"Improving Forecasts of State Failure,\" World Politics, Vol. 53, No. 4 (July, 2001): 623-58. (Article: PDF | Abstract: HTML) See also: International Conflict本研究聚焦国际冲突量化研究中一个广为人知却鲜有探讨的问题：尽管已有海量数据集、顶刊成果与精细化分析，但国际冲突领域现有文献的实证研究结论往往难以令人满意。不同研究与不同模型设定下的统计结果常存在显著差异，且目前尚无精准的冲突预测成果。针对这一问题的成因，本文提出一项猜想：那些被理论认为重要、但实证中往往被发现影响微弱且短暂的冲突诱因，对绝大多数国家二元组（dyads）而言确实影响微小；但在事前冲突概率较高的场景中，这些诱因的效应却显著、稳定且可复现。这一简洁的猜想蕴含着一系列未被充分发掘的可检验推论，且均与现有文献结论相符。我们通过构建涵盖核心特征的统计模型，直接对该猜想进行了检验。我们所采用的"neural network"模型变体，揭示了国际冲突中若干值得关注的结构性特征；作为一项评估指标，其预测性能远超此前所有同类研究。此外，该模型的性能提升所需的计算成本极低，且可便捷地检验该模型相较于主流简易模型是否具备统计层面的显著优势。本文荣获戈斯内尔奖（Gosnell Prize）。另可参阅本文针对同行评议评论的回应：Beck, Nathaniel; Gary King; 与Langche Zeng, "Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski," American Political Science Review, 98, 2 (May, 2004): 379--389 (Article: PDF | Abstract: HTML</a>), and a related paper King, Gary; Zeng, Langche, "Improving Forecasts of State Failure," World Politics, Vol. 53, No. 4 (July, 2001): 623-58. (Article: PDF | Abstract: HTML) 另参阅：国际冲突