An Ordinal Latent Variable Model of Conflict Intensity

TL;DR

This paper introduces a probabilistic ordinal latent variable model that incorporates contextual information like casualties to better measure conflict intensity from event data, improving upon existing expert-based scales.

Contribution

It proposes a novel ordered latent variable model that captures conflict intensity by integrating multiple data aspects, advancing quantitative conflict analysis methods.

Findings

Model achieves good predictive performance on held-out data.

Incorporates casualty counts and other context for more accurate intensity measurement.

Outperforms traditional expert-based scoring methods.

Abstract

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that scores events on a conflictual-cooperative scale. It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity". This paper takes a latent variable-based approach to measuring conflict intensity. We introduce a probabilistic generative model that assumes each observed event is associated with a latent intensity class. A novel aspect of this model is that it imposes an ordering on the classes, such that higher-valued classes denote higher levels of intensity. The ordinal nature of the latent variable is induced from naturally ordered aspects of the data (e.g., casualty counts) where higher values naturally indicate higher intensity. We evaluate the proposed model both intrinsically and extrinsically, showing that it obtains comparatively good held-out predictive performance.