On the statistical properties and tail risk of violent conflicts

TL;DR

This paper applies advanced statistical methods to historical conflict data, revealing that tail risks of violent conflicts are underestimated and challenging the notion of a long-term decline in violence.

Contribution

It introduces a novel application of extreme value theory to bounded variables like war casualties, providing more accurate tail risk estimates.

Findings

Real mean casualties are higher than naive estimates.

No clear trend in inter-arrival times of tail events.

Violence decline over time is not supported by data.

Abstract

We examine statistical pictures of violent conflicts over the last 2000 years, finding techniques for dealing with incompleteness and unreliability of historical data. We introduce a novel approach to apply extreme value theory to fat-tailed variables that have a remote, but nonetheless finite upper bound, by defining a corresponding unbounded dual distribution (given that potential war casualties are bounded by the world population). We apply methods from extreme value theory on the dual distribution and derive its tail properties. The dual method allows us to calculate the real mean of war casualties, which proves to be considerably larger than the sample mean, meaning severe underestimation of the tail risks of conflicts from naive observation. We analyze the robustness of our results to errors in historical reports, taking into account the unreliability of accounts by historians and absence of critical data. We study inter-arrival times between tail events and find that no particular trend can be asserted. All the statistical pictures obtained are at variance with the prevailing claims about "long peace", namely that violence has been declining over time.