How ‘Game Of Thrones’ Will Predict The Next Osama bin Laden
Patrick Tucker had a July 29, 2014 article in the online publication DefenseOne.com, with the title above. He begins by asking, “How do you predict the terror leader of the future?” “In sort of the same way you can predict what happens next on Game of Thrones, — applied statistics,” he writes. “A research team from the University of Maryland has devised a system to ‘predict’ — the top three players — (out of hundreds), who are most likely to become the new leader of a given terror group will become more dangerous after the succession; or less so, and how the terror network will evolve as a result,” Mr. Tucker wrote.
Mr. Tucker writes that “University of Maryland computer scientists V.S. Subrahmanian, Francesca Spezzano, and Aaron Mannes, called their method “Shaping Terrorist Organization Network Efficiency, or STONE.” “Their findings are published in the August 2014 issue of Communications of the ACM.
“Using carefully cultivated, open-source data on four terrorists networks, al-Qaeda, Hamas, Hezbollah, and Lashkar e-Taiba (the group associated with the 2008 terror attacks in Mumbai, India),” Mr. Tucker writes that the researchers “looked to explain how the removal of specific individuals would change the group — in order to predict who would rise in the organization — if that leader were removed,” wrote Mr. Tucker. “Some of the variables include the role that the leader plays, such as fundraiser, spiritual leader, or, recruiter, how dangerous he was on the basis of hostility and capability; and, the potential for rehabilitation in the event he was taken out.”
“This application of statistical weights and measures to group dynamics, is called network theory,” Mr. Tucker wrote. “It’s the sort of analysis that the FaceBook Data Science Team is constantly undertaking,” he added, “to see how people influence one another on the site, such as which of your friends can influence whether you like specific products or brands.”
“In larger groups, with hierarchical structures, traditional network theory puts a very high emphasis on what’s called centrality,” Mr. Tucker notes, “the influence of the centermost node, i.e., the terror leader, and his closest affiliations.” “Identifying key factors in networks has been studied extensively using centrality measures,” the researchers wrote. But, according to Subrahmanian, “fixating on centrality, to the exclusion of other potential dynamics is particularly problematic when analyzing terror networks — because these networks are too dynamic.”
“So, what are the variables that indicates who rises to be king terrorist — when the leader is removed?,” asks Mr. Tucker. “It’s a combination of influence, connectedness (captured by a clustering co-efficient score) and rank,” he wrote. “Just like Game of Thrones, ascension in terror networks is too often determined by rank, rather than other leadership criteria. “The rank of the person in the organization played a more important role than I expected,” Subrahmanian said.
Mr. Tucker writes that “the conclusion may seem intuitive, but it’s why analyzing other network variables, and how a given terror group evolves when the leader is taken out, — is important. Too often, government and anti-terror forces, overestimate the damage that they can cause a terrorist group by removing a leader. They don’t [adequately] consider the possibility of a terror group actually growing stronger,” with a change in leadership. “Removal of incompetent leaders can actually lead to a more lethal organization…or, if he is incompetent, he may be replaced by someone much more deadly,” Subrahmanian said.
“In the paper,” Mr. Tucker wrote, “the researchers represent this through what they call a lethality function. It reveals how removing certain nodes will change the overall group dynamic; and, possibly generate networks around leaders of lower rank — but, higher scores of influence, or connectedness, which is essentially what happened with the Islamic State of Iraq and the Levant, or ISIL; also known as ISIS.”
“So, if analysts typically overplay the value of rank, what are they underplaying?,” asks Mr. Tucker.
“Of the three variables that make a suitable terror leader,” he writes, “connectedness, the clustering of co-efficient scores, is a particularly interesting one, measuring the cohesive of an individual’s network. In everyday terms, your clustering co-efficient is high if all your friends on FaceBook are connected to one another,” he noted.
“In the world of al-Qaeda, while Ayman al Zawahiri was most likely to ascend to the top leadership position — on the basis of rank, Abu Bakr al-Baghdadi, you could say, had forged the best clustering co-efficient score,” Mr. Tucker observed. “Similarly,” he adds, in the popular Game of Thrones books, the character of Joffrey Baratheon is an example of a node with high rank, but low scores in influence and connectedness. The diabolical Tywin Lannister has a high influence, enabling him to effectively determine the course of events — despite not being king. Jon Snow, meanwhile, has a high clustering, co-efficient score. He’s connected to the men of the Night’s Watch, who are all connected to one another,” wrote Mr. Tucker.
“It’s a theoretical problem, with real world implications,” contends Mr. Tucker. “This paper clearly pushes back at the idea that military leaders couldn’t possibly have predicted the rise of ISIL. In addition to modeling, the most likely ascension dynamics in a group, attack data can also yield insight into how dangerous a terrorist group might become if it is disturbed in the wrong way, or at the wrong time.” “When an analyst recommends a set of nodes to be removed from a network (e.g., in a capture operation), he must consider both the possible new networks that result, and their lethality,” the researchers wrote. “That’s why the lethality function is so important, as it measures the relationship between the structure of the network, and number of attacks.”
Subrahmanian reports that the STONE method predicts lethality with a Pearson correlation co-efficient of .83 for Laskar-e-Taiba, and .65 for al-Qaeda. A Pearson correlation co-efficient is a bit like a percentage; but, the scale is from -1, which is a totally negative correlation, to +1, a near perfect correlation, — so, .83 is very good,” wrote Mr. Tucker. “The slightly less good score for al-Qaeda,” he says, “was the result of data paucity. “The accuracy increases, as the size of the network increases. With more [quality] data, you can predict things better.”
“Some of that data might consist of edge analysis, specifically how different individuals are relating to one another. This can help better determine influence,” Mr. Tucker wrote. “It’s the sort of analysis that Jure Leskovec has been pioneering at Stanford — that can be observed playing out in real-time in web forums, on FaceBook, and even in Wikipedia edits,” he noted.
“Of course, any research published out in the open could tip off the enemy. But, Subrahmanian doesn’t consider that a problem, so much as an opportunity,” notes Mr. Tucker. “Should al-Qaeda read this paper; and, understand it thoroughly, how might they restructure themselves — to keep themselves more secure, against the kind of analysis we’re doing? We’ve shown that disclosing some rules can shape the behavior of your adversary, “in a way you can influence them,” Subrahmanian said. “In other words, you can game al-Qaeda with game theory, which is also pretty Game of Thrones. The potential value of this sort of enhanced clairvoyance, is on display right now in the events playing out in the Middle East. Before there was ISIL, there was al-Qaeda in Iraq, a terror group headed by Abu Massab al-Zarqawi. When the U.S. military took Zarqawi out, with a drone attack in 2012, the Pentagon celebrated until a new head grew in the place of the old one, that of al-Baghdadi, leader of ISIL, sometimes called the most dangerous man on earth.”
“Subrahmanian said he and his team will be applying the methodology in the months ahead, in a new way — not just to prove that it works — but, to actually predict the characters most likely to become the next terrorist mastermind.”
Interesting work and study. Like anything else, it isn’t perfect — and, is never likely to be — in our lifetimes. Too many unknowns and variables come into play: blind luck, in the right place, at the right time, late-bloomers, unexpected courage, failure of the opponent or competition to take advantage or exploit a situation or event, etc., etc. Much like big data mining software and algorithms, this method/technique isn’t fail-proof; and, should be used and treated as an additive to the entire analytical picture; but, not to the point that analysts and others begin to exclude potential leaders and wildcards — just because they didn’t neatly fit into the parameters of this method. It would also be interesting to know — how successful, or not, this technique might have been in predicting the rise and fall of the Mafioso and the New York crime syndicates. Clearly, this method/technique is dependent on exquisite/detailed data about family relationships, connections, etc. and — I suspect generally requires much more detailed data than we typically get or know about a group or person. Having said all that, it is a method, or technique that shouldn’t be ignored; and, included in our kit-bag of techniques when attempting to predict or guess the next….Osama bin Laden. V/R, RCP