The distribution of violent event and interevent times in conflicts

TL;DR

This paper investigates the distribution of violent event interevent times, comparing power law and lognormal models, and finds that fine-grained data challenges previous assumptions about their distribution.

Contribution

It demonstrates that fine-grained data of violent events do not conform to the predicted lognormal distribution, challenging existing mathematical predictions.

Findings

Power law distributions fit coarse-grained interevent times.

Fine-grained data does not fit lognormal better than power law.

Violent event durations are lognormally distributed.

Abstract

Enduring violent conflicts are interrupted by lulls without violence. Studies of interevent times found power law distributions based on coarse-grained data with a resolution of one day. Fine-grained data of violence with a resolution of seconds or shorter is rare. A mathematical theorem predicts that the distributions thereof is lognormal, not power law. However, when violent conflicts are represented as multiplicative processes and fine-grained data is used, the log normal does not fit better than the power law. Therefore, common wisdom is not refuted. Violent events, by contrast, take much energy and their durations are shorter, hence they are lognormally distributed.