Replication data for: Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski

We thank Scott de Marchi, Christopher Gelpi, and Jeffrey Grynaviski (2003; hereinafter dGG) for their careful attention to our work (Beck, King, and Zeng, 2000; hereinafter BKZ) and for raising some important methodological issues that we agree deserve readers' attention. We are pleased that dGG's analyses are consistent with the theoretical conjecture about international conflict put forward in BKZ --- \"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 whenever the ex ante probability of conflict is large\" (BKZ, p.21) --- and that dGG agree with our main methodological point that out-of-sample forecasting performance should always be one of the standards used to judge s tudies of international conflict, and indeed most other areas of political science. However, dGG frequently err when they draw methodological conclusions. Their central claim involves the superiority of logit over neural network models for international conflict data, as judged by forecasting performance and other properties such as ease of use and interpretation (\"neural networks hold few unambiguous advantages... and carry significant costs\" relative to logit; dGG, p.14). We show here that this claim, which would be regarded as stunning in any of the diverse f ields in which both methods are more commonly used, is false. We also show that dGG's methodological errors and the restrictive model they favor cause them to miss and mischaracterize crucial patterns in the causes of international conflict. We begin in the next section by summarizing the growing support for our conjecture about international conflict. The second section discusses the theoretical reasons why neural networks dominate logistic regression, correcting a number of methodological errors. The third section then demonstrates empirically, in the same data as used in BKZ and dGG, that neural networks substantially outperform dGG's logit model. We show that neural networks improve on the forecasts from logit as much as logit improves on a model with no theoretical variables. We also show how dGG's logit analysis assumed, rather than estimated, the answer to the central question about the literature's most important finding, the effect of democracy on war. Since this and other substantive assumptions underlying their logit model are wrong, their substantive conclusion about the democratic peace is also wrong. The neural network models we used in BKZ not only avoid these difficulties, but they, or one of the other methods available that do not make highly restrictive assumptions about the exact functional form, are just what is called for to study the observable implications of our conjecture. This paper is a response to a comment on Beck, Nathaniel; King, Gary; and Zeng, Langche, 2000, \"Improving Quantitative Studies of International Conflict: A Conjecture,\" American Political Science Review, Vol. 94, No. 1, 21-36. (Article: PDF | Abstract: HTML) See also: International Conflict

我们感谢Scott de Marchi、Christopher Gelpi与Jeffrey Grynaviski（2003年，下文简称dGG）对我们的研究（Beck、King与Zeng，2000年，下文简称BKZ）给予的细致关注，并提出了若干重要的方法论议题——我们认同这些议题值得读者关注。我们欣喜地发现，dGG的分析与BKZ中提出的国际冲突理论猜想相一致：“那些被理论认定为重要、但实际往往影响微弱或短暂的冲突成因，在绝大多数国家二元组中确实影响微小；但当冲突的事前概率较高时，这些成因的影响则显著、稳定且可复现”（BKZ，第21页）。同时，dGG也认同我们的核心方法论观点：样本外预测表现始终应当作为评判国际冲突研究乃至绝大多数政治学领域研究的标准之一。然而，dGG在得出方法论结论时屡屡出现谬误。其核心主张认为，基于预测表现与易用性、可解释性等其他特性来看，逻辑斯蒂回归模型（logit）优于用于国际冲突数据的神经网络（neural network）模型——dGG称“相较于逻辑斯蒂回归，神经网络几乎没有明确优势……且存在显著缺陷”（dGG，第14页）。我们在此证明，这一主张在两种方法均被广泛应用的诸多领域中都会被视为极具颠覆性，而实际上该主张是错误的。我们还将证明，dGG的方法论谬误及其所支持的限制性模型，导致其未能捕捉到国际冲突成因中的关键模式，甚至对这些模式做出了错误描述。我们将在下一部分首先总结学界对我们的国际冲突猜想日益增多的支持证据。第二部分将从理论层面阐释神经网络为何优于逻辑斯蒂回归，并纠正若干方法论谬误。第三部分则基于BKZ与dGG所用的同一组数据，从实证层面证明神经网络模型的表现显著优于dGG的逻辑斯蒂回归模型。我们将证明，神经网络对逻辑斯蒂回归预测效果的提升幅度，等同于逻辑斯蒂回归对不含理论变量的基准模型的提升幅度。我们还将阐明，dGG的逻辑斯蒂回归分析并未对学界最重要的研究发现——民主对战争的影响——这一核心问题的答案进行估计，而是直接预设了该答案。由于其逻辑斯蒂回归模型所基于的这一假设及其他实质性假设均存在错误，因此他们关于民主和平论的实质性结论同样站不住脚。我们在BKZ中使用的神经网络模型不仅规避了上述问题，而且这类模型，或是其他无需对精确函数形式做出极强限制性假设的现有方法，恰恰契合了用于检验我们猜想的可观测推论的研究需求。本文是对一篇针对Beck、Nathaniel、King、Gary与Zeng、Langche 2000年发表于《美国政治科学评论》第94卷第1期第21-36页的《改进国际冲突的量化研究：一项猜想》一文的评论的回应。（文章：PDF | 摘要：HTML）另见：国际冲突。