When political scientist Jason Lyall of Yale University in the United States surveyed the mood of villages strewn across the country’s southern provinces he found that those with the most pro-US feeling were the most likely to draw punishment attacks from the Taliban. Worse, the US was no more likely to find improvised explosive devices (IEDs) in those supportive villages. 
The dynamics behind this are not totally clear. But the implication is that US efforts to win villagers’ hearts and minds were successful enough to render their villages Taliban targets, but not enough to convince them to provide useful intelligence about IEDs. If true, the military is thwarting its own aim, stated in the US Army Field Manual, of “creating safe spaces for the population by reducing insurgent attacks”.
It’s a suggestion so controversial that Lyall and his team are still working to convince themselves — and their paper’s peer-reviewers — that civilian attitudes could influence attack predictions so strongly.
But even at this early stage it’s a powerful example of the insights that the emerging field of violence forecasting could yield. That’s because Lyall’s results come from an algorithm that takes in information from village surveys and spits out predictions for where violence will occur.
Statistical and computer models that predict behaviour might sound like science fiction, but several groups are doing similar research. In doing so they are identifying possible causes of conflict, raising hopes of prevention, and potentially providing guidance on safety and stability for development work.
The few existing efforts to predict violence typically use prior incidents to forecast future ones. To improve on this, in early 2011 Lyall’s team surveyed 2,754 men in 204 Afghan villages about their level of support for the Taliban and the International Security Assistance Force (ISAF). They combined this information with data on insurgent violence and the locations of military bases and aid projects.
The researchers built these factors into a statistical model. It showed that villages’ levels of support for ISAF predicted IED attacks occurring in the 15 kilometres around each village for up to the next ten months. A village showing ‘modest’ ISAF support would suffer 13 extra attacks on average over the following five months than one strongly opposed to ISAF.
Lyall and his colleagues tested this relatively simple statistical model on another 14,606 villages they hadn’t surveyed. They combined data on previous incidents in these villages with attitude estimates extrapolated from the surveyed villages, and improved IED attack predictions by up to 30 per cent.
Lyall stresses, however, that field tests must be run and peacekeepers and police need to cooperate more closely with researchers before they can trust these estimates enough to use them.
The survey itself cost around US$150,000, which Lyall admits is high, but relatively cheap by the standards of surveys in places such as Afghanistan. Cheaper methods can be used once a model is built.
“Targeted surveys with purpose-built questions are likely to have a higher predictive payoff than large-scale surveys at a fraction of the cost,” Lyall says. He is also confident this approach could build prediction models for other types of violence.
Both the military and NGOs have shown an interest in the technique, says Lyall. “Being able to predict future violence would allow development agencies to select areas for greater aid effectiveness,” he explains. The Africa-centred Ushahidi collaboration’s crowd-sourced CrisisNet could provide ideal ‘micro-level data’ for prediction, he says.
Other research from Yale strengthens the case for using survey-based statistical predictions of violence.
In a paper undergoing peer-review, Yale’s Robert Blair and his colleagues predict violence such as murders and rapes in Liberia.  The team was already surveying 242 Liberian communities about violence and dozens of geographic social, and economic factors for a randomised controlled trial (RCT). After donors asked Blair’s team if the data could forecast violence, he found that one model correctly predicted 88 per cent of violent incidents using just five variables.
One variable that appeared to be linked to violence was the prevalence of power-sharing agreements where minority ethnic groups were given a say in local governance. This is interesting because political scientists have traditionally recommended power sharing as good way to help avoid conflict.
“The places we care most about may be the places it’s hardest to get data on.”
Robert Blair, Yale University
Blair’s models appear to cast doubt on this, much like Lyall’s team’s study does on the ‘hearts and minds’ appraoch. Blair says that such models could help evaluate other received wisdom from politics about peace-keeping, as political science often lacks the means to test its explanations.
Collecting the data was “laborious, expensive and slow”, Blair admits, and the work must be replicated in each country before predictions can be made. However he is optimistic that if and when others confirm their findings in Liberia, exploiting the results need not be expensive.
“Once you have an accumulation of studies, you can narrow down to fewer and fewer variables,” he explains. “Then you can design, for instance, a cellphone-based survey with local leaders every couple of weeks. That’s cheap.”
Blair is now working towards such projects with a consortium of NGOs and governmental organisations called the Early Warning-Early Response (EWER) working group in Liberia. EWER’s activities include the interactive Liberia Early-Warning and Response Network (LERN) map, developed by Ushahidi to detect and avert possible conflict.
“They have a lot of folks in the field but they don’t have a very systematic way to generate insights from observations,” says Blair. “We’re working to see how we can systematise that.”
Blair is now hoping to create violence-forecasting models using data from surveys in Indonesia and Iraq. He’s also involved with an effort to forecast violence on a very different scale, with the Political Instability Task Force (PITF) funded by the US Central Intelligence Agency.
They have a large data set, gleaned from a program that trawls the internet gathering information from the language people use online, Blair explains.
Although the Liberia data set is small by comparison, Blair believes it’s still competitive with this ‘big data’ approach.
The PITF’s programmes “vacuum the internet and code news stories into data points, but the algorithm is messy”, he says. “You don’t know to what extent you’re modelling noise.”
“We stick to the opposite approach. If an instance of violence occurs we’re going to get as positive as we can that it really did occur. Plus, in a place like Liberia there isn’t that much media online for algorithms to scrape. That’s probably true of places like Afghanistan, Sub-Saharan Africa and the Middle East — the places we care most about may be the places it’s hardest to get data on.” – See more at: http://www.scidev.net/global/conflict/feature/modelling-mob-computers-predict-violence.html#sthash.OjlG5QTc.dpuf