Catching the trigger?

Including automated event data in interstate conflict prediction

Master thesis (2023)

Authors

J.O. Herlé Technology, Policy and Management

Contributors

J. Zatarain Salazar Policy Analysis - TPM (mentor)
Cees van Beers Economics of Technology and Innovation - TPM (mentor)
Tim Sweijs The Hague Centre for Strategic Studies (coach)
Faculty
Technology, Policy and Management, Technology, Policy and Management
Copyright: © 2023 Olivier Herle
More Info
expand_more

Published Date

23-03-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Technology, Policy and Management

Abstract

This thesis evaluates the effects of including automated event data for interstate conflict prediction. Automated event data are web-scraped news stories converted into data and they may allow conflict models to increase their performance. Accurate models can then be used for early-warning purposes.

To predict three separate problems, tree ensemble classifiers were used. The three outcomes to be predicted were the occurrence of interstate conflict, its onset, and its escalation. They were predicted globally at the dyad month level, meaning monthly for every country pair, using data from 1995 to 2014. The feature set consisted of eleven structural, slow-changing variables, and 268 event features, which were event counts on a dyad month according to event type.

The analysis showed that event data did not increase performance. This held across all three prediction problems. Additionally, it was found that the models for occurrence and escalation and performed well and decently well, respectively, but that the models for conflict onset performed poorly.

In conclusion, event data needs further testing in different constellations to be effective in interstate conflict prediction. It seems likely, however, that effective prediction for policy guidance is possible, given the model performance of the occurrence and escalation models.