An interesting article about analyzing and modeling data from wars and conflicts was published in Nature yesterday. On first glance, it looked like one of those “look, we found another power law” papers, but after I had read this interview with one of the authors, I changed my mind – it’s really quite interesting.
The authors compiled “… a collection of state-of-the-art datasets for a wide range of modern wars from […] a range of sources including NGO reports, media streams, governmental databases and social scientists who are experts in specific conflicts. […] The result was a database of over 54,000 unique events covering 11 different wars. The data collection method utilized an open-source intelligence methodology.”
Even this data-collection effort is interesting in itself. According to Sean Gourley (the researcher interviewed in the link above), the statistical pattern remains similar no matter what data source (governmental/academic/mass media) you are looking at.
One of the important results of the statistical analysis was that there is a “… common pattern in both the size and timing of violent events within modern insurgent wars […] observed […] across multiple different conflicts from Iraq to Sierra Leone [and] independent of geography, ideology, politics or religion.” The patterns are not observed in older wars and thus seem to be unique to modern wars.
Also, attacks aren’t randomly distributed across a conflict but tend to be clustered together. Gourley has an intriguing explanation for this: “The cause of this clustering is coordination via a global signal and competition amongst groups for media exposure and resources.”
The authors also went beyond the statistical analysis and set up a mathematical model describing the structure of conflicts – they even call it a “unified theory of insurgency“.
The interview with Gourley mentions a friend of a friend, Aaron Clauset, who has done similar work on the statistics of terrorist attacks. Incidentally (coming back to the ubiquitous “x is power-law distributed” papers I mentioned in the beginning), one of Aaron’s papers contains very useful methodology for ruling out that a distribution is a power law.