Send to a friend
A statistical model that can accurately predict where violence will occur in Liberia has been developed. The advance raises the possibility that developing countries could use similar models to target peacekeeping measures at predicted hotspots, potentially preventing violence escalating into regional clashes or wars.
Statistical models that point to where violence may occur already exist, but they typically use ‘big data’ sources — such as databases of news reports or social media — to predict where regional or national violence might take place at short notice.
But those models break down where insufficient data is available, for example at the level of villages with small populations or in countries where news reports are unreliable and few people use social media.
Political scientist Robert Blair of Yale University, United States, one of the new model’s developers, says he was particularly keen to see if it was possible to predict violence at the level of small towns or villages because interventions at that point could stop small incidents from escalating into regional clashes or even civil war.
In Liberia, the courts and police are “very weak — inaccessible and corrupt — so citizens don’t have institutions that can adjudicate when fist fights or murders happen”, he says.
That means minor clashes, especially those involving ethnic factions, can quickly spread to whole regions. “It’s precisely these small-scale incidents that you want to be able to predict in order to stop them escalating into these much larger problems which can have longer-term national implications,” says Blair.
Targeting at-risk areas
A refined version of the model Blair and his colleagues have developed — described in a working paper published online this month on the Social Science Research Network — could suggest ways of deploying Liberia’s underfunded and understaffed police force in the most at-risk areas, he says.
Blair began working on the model in 2009 with fellow Yale researcher Alexandra Hartman and Christopher Blattman of Harvard University, United States. At the time, the team was working on a Liberian government-funded evaluation of a programme to resolve village disputes.
As part of this, the researchers surveyed 242 villages and towns across Liberia’s violent northern border in 2008 and 2010. They gathered a wide range of information on potential risk factors from community representatives, from the number of people who accept interreligious marriage to the number who believe other tribes are ‘dirty’.
Alongside that, they asked local leaders in 2010 and 2012 about any instances of violence in their village or town that year.
Village or town population.
Is an ethnic minority group involved in village or town leadership?
Proportion of residents who believe other tribes are violent.
Proportion of residents in the dominant ethnic group.
Proportion of residents who contribute to public facilities.
The five variables that together predicted 88 per cent of violent incidents in the Liberian villages studied
Blair and the team then plugged the 2008 survey results into their statistical models. This showed which variables tallied most closely with the violence that happened in 2010. Then they repeated the exercise with the survey results from 2010 and used it to predict violence that occurred in 2012, which they measured separately.
They found that one model correctly predicted 88 per cent of violent incidents that occurred using just five variables (see box).
The research hints that it could be easier than previously thought to collect specific data to predict violence in other nations. That is “because we’re not collecting data across 87 different variables, we’re really looking at these four or so things,” says Michael Kleinman, director of investments at NGO Humanity International, which part-funded the research.
But he notes that fresh research would be needed to confirm whether a similarly small selection of risk factors could predict violence in other nations or in special circumstances such as the current Ebola crisis.
“Exactly the information we need”
The 88 per cent correct prediction rate comes at the cost of many false positives — instances where violence is predicted, but never occurs — says Blair. Yet he does not necessarily see this as a problem.
What it means, he says, is that “the Liberian government could say: ‘Hey, we have the resources to send cops to ten villages. Can you tell us what are the ten villages with the highest risk of violence?’ Well, yes, the model can do it.”
Nathaniel Walker, an independent consultant with the Liberia Peacebuilding Office, part of the country’s Ministry of Internal Affairs, agrees. He says that, if proven right, the model “will provide exactly the type of information we need to do more robust monitoring”.
If the peacekeeping office knew where violence was most likely to occur, says Walker, it could use its limited resources to focus preventative activities there.
The research holds one more lesson. The best violence predicting variable was whether or not an ethnic minority group was involved in village leadership. Blair says that when he first saw that he “was so surprised that I thought I must have miscoded something” because “we would expect power sharing to mitigate the risk of violence, not exacerbate it”.
According to Blair, power sharing agreements are a common strategy for dealing with ethnic conflict. This study does not prove that such efforts are ineffective, he says, but it should perhaps prompt a re-examination of whether policymakers should use them as a go-to solution for dealing with ethnic strife.