What is Escalation? Measuring Crisis Dynamics in International Relations with Human and LLM Generated Event Data

TL;DR

This paper introduces a data-driven approach to measure crisis escalation in international relations by analyzing actions in 475 crises using both human and AI-generated data, providing insights into conflict dynamics.

Contribution

It combines human-coded and machine-coded datasets to empirically model and interpret the escalation or de-escalation effects of actions in international crises.

Findings

Actions can be quantitatively linked to crisis duration

Machine coding aligns with human assessments of escalation

Provides a new interpretable measure of crisis dynamics

Abstract

When a dangerous international crisis begins, leaders need to know whether their next move is going to resolve the dispute or amplify it out of control. Theories of conflict have mainly served to deepen the confusion, revealing fighting, bargaining, and signaling to be high-dimensional and subtle equilibrium behaviors with deeply contextual consequences. Should a leader communicate resolve through aggressive acts, avoid spirals through accommodation, or focus on ensuring the possibility of a bargain? We offer a data-driven empirical solution to this logjam in the form of a new large-scale analysis of actions taken within 475 crises. We combine two complimentary measurement projects, the human-coded International Crisis Behavior Events (ICBe) dataset and the new machine-coded ICBeLLM. We model directly whether an action tends to shorten or extend the length of a crisis. The result is a directly interpretable measure of the latent escalatory/de-escalatory nature of each action leaders have chosen over the last century.