Do the robot: Lessons from machine learning to improve conflict forecasting

Increasingly, scholars interested in understanding conflict processes have turned to evaluating out-of-sample forecasts to judge and compare the usefulness of their models. Research in this vein has made significant progress in identifying and avoiding the problem of overfitting sample data. Yet there has been less research providing strategies and tools to practically improve the out-of-sample performance of existing models and connect forecasting improvement to the goal of theory development in conflict studies. In this article, we fill this void by building on lessons from machine learning research. We highlight a set of iterative tasks, which David Blei terms 'Box's loop', that can be summarized as build, compute, critique, and think. While the initial steps of Box's loop will be familiar to researchers, the underutilized process of model criticism allows researchers to iteratively learn more useful representations of the data generation process from the discrepancies between the trained model and held-out data. To benefit from iterative model criticism, we advise researchers not only to split their available data into separate training and test sets, but also sample from their training data to allow for iterative model development, as is common in machine learning applications. Since practical tools for model criticism in particular are underdeveloped, we also provide software for new visualizations that build upon already existing tools. We use models of civil war onset to provide an illustration of how our machine learning-inspired research design can simultaneously improve out-of-sample forecasting performance and identify useful theoretical contributions. We believe these research strategies can complement existing designs to accelerate innovations across conflict processes.