Analysing international events through the lens of statistical physics: the case of Ukraine

TL;DR

This paper applies statistical physics tools to analyze violent events in Ukraine since 2021, revealing complex spatial and temporal patterns and challenging simplified regional narratives.

Contribution

It introduces a novel application of entropy, complexity metrics, and functional networks to political violence data, providing new insights into Ukraine's conflict dynamics.

Findings

Events are non-randomly distributed over time and space.

Eastern Ukraine is functionally disconnected from other regions.

The analysis challenges the 'two Ukraines' narrative.

Abstract

During the last years, statistical physics has received an increasing attention as a framework for the analysis of real complex systems; yet, this is less clear in the case of international political events, partly due to the complexity in securing relevant quantitative data on them. Here we analyse a detailed data set of violent events that took place in Ukraine since January 2021, and analyse their temporal and spatial correlations through entropy and complexity metrics, and functional networks. Results depict a complex scenario, with events appearing in a non-random fashion, but with eastern-most regions functionally disconnected from the remainder of the country -- something opposing the widespread "two Ukraines" view. We further draw some lessons and venues for future analyses.