Quantitative patterns in drone wars

TL;DR

This paper analyzes the statistical patterns of drone attacks, revealing they follow different distributions than other conflicts and are influenced by resource control dynamics, modeled through a feedback loop.

Contribution

It introduces a novel quantitative analysis of drone attack patterns, highlighting their unique distributional characteristics and underlying control dynamics.

Findings

Drone attack severity follows lognormal and exponential distributions.

Attack timing suggests one side has near-complete control.

A mathematical model reproduces observed attack patterns.

Abstract

Attacks by drones (i.e., unmanned combat air vehicles) continue to generate heated political and ethical debates. Here we examine the quantitative nature of drone attacks, focusing on how their intensity and frequency compare with that of other forms of human conflict. Instead of the power-law distribution found recently for insurgent and terrorist attacks, the severity of attacks is more akin to lognormal and exponential distributions, suggesting that the dynamics underlying drone attacks lie beyond these other forms of human conflict. We find that the pattern in the timing of attacks is consistent with one side having almost complete control, an important if expected result. We show that these novel features can be reproduced and understood using a generative mathematical model in which resource allocation to the dominant side is regulated through a feedback loop.