A Nature paper on human insurgency

Common ecology quantifies human insurgency
by Bohorquez, Gourley, Dixon, Spagat, and Johnson
(CEIBA complex systems research center in Columbia, etc)

The paper presents an empirical data analysis of 9 major insurgencies, consisting of 54,679 events worldwide. The paper centers around the theme of finding a common factor across different insurgent conflicts and proposes a model of conflict organization, which treats insurgent population as an ecology of dynamically evolving, decision-making groups. Although groups are heterogeneous in terms of their strategies, they tend to converge towards similar response when fed the same information.

Finding (a) Insurgent conflicts have different characteristics from the traditional wars: their scaling factor is around 2.5 and typically show a power-law trend (rejects log-normality), while traditional wars cannot reject log-normality and show a smaller scaling factor. The implication of this scaling factor (2.5) is at its robustness. (It's hard to fragment such insurgent group.)
Finding (b) Burstiness in terms of the number of events per day shows abundance of heavy and light days, or conversely, lack of 'medium' days. The implication of this burstiness is at maximizing the media attention. (Modern insurgent conflicts are not about killing others, but about media show. It's better to attack on a quiet day)

Model assumes two mechanisms: (a) on-going group dynamics within the insurgent population (coalescence and fragmentation of members to different groups over time) (b) group decision making about when to attack

Personal note: I read this paper twice. At first, the model seemed too simple and I wasn't really impressed by the work. Given the general theme of the paper, I expected there to be lots of sophisticated ideas and theories that people have looked at. The model in the paper, however, was extremely simple. After a second read, I starting seeing how a simple model could link together many different empirical patterns the paper showed (e.g., why insurgencies occur around a scaling factor of 2.5, why the distribution of Columbian and Afghanistan insurgencies have different tail shapes.)

Among the tricks I've learned from the paper, I liked the frequency distribution of real wars against random wars. 10,000 random wars were generated such that dates of the events were shuffled and the average frequency was taken across the samples. Authors then compare the frequency distribution of actual data with this randomly shuffled wars. I also liked the link between this work and the same patterns shown in financial data (how people herd around different stock items each day) -- it might really show a crucial link between violent and non-violent forms of human behavior.

Now moving forward, how could we utilize the result of this work to other social network data like Twitter? Insurgency, rallies, and demonstration -- all seem difficult in terms of parsing data. But if we can do anything, what would I want to look at? Adopting methodology is an obvious way, but could we do something cooler?