Improving Quantitative Studies of International Conflict: A Conjecture

By Nathaniel N. Beck, San Diego, Gary King, Langche Zeng. 2000

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 includes its 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.

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Citations in the corpus

Missing Data Imputation for Supervised Learning (2016, background): “…an increasingly attractive methodology and proven to be eﬀective in social science applications, such as studies of international and civil conﬂict (Beck et al., 2000; De Marchi et al., 2004; Hill and Jones, 2014; Muchlinski et al., 2016) and election fraud (Cant´u and Saiegh, 2011; Montgomery…”
Explaining Rare Events in International Relations (2001, unknown): “Beck, King, and Zeng 2001.”
Estimating individual treatment effect: generalization bounds and algorithms (2016, methodology): “Neural nets have also been used for this purpose, exemplified in early work by Beck et al. (2000), and more recently by Hartford et al. (2016)'s work on deep instrumental variables .”
Logistic Regression in Rare Events Data (2001, methodology): “With the additional information brought in by these methods, in combination with more flexible and highly interactive functional forms (Beck et al. 2000), perhaps the quantitative study of war will become more feasible.”
Saving Human Lives: What Complexity Science and Information Systems can Contribute (2014, unknown): “…(e.g., arms races [90], long-standing territorial rivalries [91], or large and rapid shifts in power [92,93]) rely on indicators that are coarse in time (typically yearly), and therefore tend to miss the crucial steps of the escalation of tensions and the timing of the conﬂict outbreak [11,94– 96].”
Improving Forecasts of State Failure (2001, methodology): “We impute the missing data and then use the neural network statistical model detailed in Beck, King, and Zeng (2000), modiied by the use of what are known as \committee methods".”
The ~Ir!Relevance of Militarized Interstate Disputes for International Trade (2002, methodology): “Second, an alternative approach is to use the “neural network” model ~Beck, King, and Zeng, 2000!. Future research may assess whether our results are sensitive to the use of different forecasting models.”
Link Prediction via Matrix Factorization (2011, background): “Both linear [33] and nonlinear [14,5] choices of fD(·), fM (·, ·) have been considered.”