A NEW MODEL FOR WAR GAMES?: If insurgent and terrorist attacks get more efficient over time, it could change how the costs of modern war and counter efforts are calculated. Image: iStockphoto/ Daft_Lion_Studio
War fatalities—and especially those from terrorist or insurgent attacks—seem particularly and cruelly random. But some scientists think they have found the key to predicting just when these deadly assaults will come.
The findings are not based on new reconnaissance technology or intelligence breakthrough, but rather on some relatively simple number crunching. As it turns out, whether it is fatal roadside bombings in Kabul in 2008 or lethal terrorist attacks from a separatist group in the 1970s, the frequency of successful strikes comes at a relatively consistent rate of escalation, according to a new paper published in the July 1 issue of in Science. And to some researchers, that rate looks an awful lot like a learning curve.
When faced with a challenging task, groups of people tend to get faster the more they do it—whether that task is building an airplane or performing multistep surgery. The same holds true, the authors of the new paper argue, for organizing and executing successful militant attacks.
After analyzing reams of publicly available data on casualties from Iraq, Afghanistan, Pakistan and decades of terrorist attacks, the scientists conclude that "insurgents pretty much seemed to be following a progress curve—or a learning curve—that's very common in the manufacturing literature," says physicist Neil Johnson of the University of Miami in Florida and lead author of the study.
"Their goal is to manufacture or produce a fatal day for the military," he explains, apologizing for the glib terminology. To engineer the next fatal attack as quickly as possible, "they are of course trying to learn from what they've done in the past," he says.
This inclination helps to explain the most striking feature of the rate curve that Johnson and his colleagues found: its direction, up. As time passed, most groups seemed to be able to increase their frequency of "successful" (that is, deadly) attacks on their targets. And that is "the particular power curve that is associated with learning," Johnson says. In other words, he adds, even for terrorists, "practice makes perfect."
To arrive at the model, Johnson and his colleagues noted the first two days with fatal attacks in any given conflict—whether it was an Afghan province or a Hezbollah suicide bombing in Israel—and the subsequent escalation of frequency with which the group executed successful attacks. (The fatality data came from Operation Iraqi Freedom from 2003 to 2010, Operation Enduring Freedom in Afghanistan from 2001 to 2010, suicide bombings from Pakistan militant and Hezbollah incidents between 1995 and 2008, and 3,143 other attacks executed between 1968 and 2008.)
The model seems to successfully predict when the next day of fatal attacks might arise just based on the time between the first two attacks and the subsequent rate of escalation. As a result, Johnson explained in a Science podcast, the researchers could predict the number of days between the 50th and 51st deadly attacks.
But if these calculations are so simple, will not the insurgents and terrorists simply steer their planning around them to thwart counter-efforts? Johnson and his colleagues are confident that such a move will not necessarily be the case for the same reason commuters, knowing when traffic is worst, join the rush hour nonetheless.
The stark upward curve also highlights the finding that as conflicts continue, fatal attacks grow increasingly likely. Although the trend is apparent through Johnson's analysis, he says that he has not heard this pattern discussed much. If knowing that adversaries become predictably more efficient over time, financial, political and human calculations of the costs of war might be tabulated differently.