PREDICTING POLITICAL HOTSPOTS: PROFESSORS' GLOBAL MODEL FORECASTS CIVIL UNREST AGAINST GOVERNMENTS
The forecast for predicting the next political hotspots could be much more accurate because of a model developed by two Kansas State University professors and a colleague in New York.
The model, named the Predictive Societal Indicators of Radicalism Model of Domestic Political Violence Forecast, is currently five for five in predicting which countries will likely experience an escalation in domestic political violence against their governments within the next five years.
"So far it's been pretty accurate," said Sam Bell, assistant professor of political science at K-State. Bell created the forecast model with Amanda Murdie, K-State assistant professor of political science, and David Cingranelli, professor of political science at Binghamton University, Binghamton, N.Y. It was developed for Milcord, an Open Innovation company that builds knowledge management solutions for federal agencies.
To date the model has successfully predicted civil unrest in Peru, Ireland, Ecuador, Italy and most recently, Tunisia. Iran is currently at the top of the list.
"What's interesting is that while our model predicts violence in countries like Honduras and Iran, it's also predicting it in western democracies," Murdie said. "For example, our model predicted violence in Ireland. That happened recently due to the International Monetary Fund bailout."
To create the forecast model, the researchers built a database using publicly available information on 150 countries. It contains the frequency and intensity of domestic political violence from 1990-2009. According to Bell, this violence includes anything from a sit-in that turns into a physical altercation to an embassy bombing.
Although other forecast models have been created, this is one of the most encompassing, Murdie said. It accounts for factors like repression, governmental aid to nongovernmental organizations, aid to countries to help build security, and Internet and mobile phone usage.
In order to forecast domestic political violence, three concepts are accounted for: coercion, coordination and capacity.
Coercion is defined by violations of physical rights. This heightens the motivation of protestors, according to Murdie.
"I think that was one of the biggest findings from our model: that adhering to basic human rights limits the political violence," she said. "In covering all these countries and in looking at this passage of time, we find that human rights crackdowns still hurt a country the most, even to this day.
"There's this tendency for government to be reactionary and crack down on political rights in order to suppress political violence, but we find that crackdowns lead to this mobilization effect where people take to the streets," she said. "The human rights crackdowns don't stop insurgency; they help fuel it."
The second concept, coordination, is how easily a domestic group can mobilize.
"Two summers ago in Iran we really saw the YouTube and Twitter effect in regard to a population's ability to coordinate and increase the level of violence," Murdie said. This mobile coordination can either quickly diffuse or escalate the level of violence.
Capacity, the third factor, is the ability of a country to project itself throughout its territory, thus limiting the intensity of domestic violence against government.
Although the current model operates at a macro level, Bell said it's possible to isolate certain countries and aggregate a much smaller time frame from the current five-year forecast. The researchers have also thought about using the database to create a risk assessment for civilian terrorism against other citizens.
The Domestic Political Violence Forecasting Model has been developed under the Predictive Societal Indicators of Radicalism project sponsored by the Air Force Research Labs Rome Research Laboratory. A list of the top 37 countries projected to experience civil unrest between now and 2014 and updates on forecast accuracy can be found online at http://radicalism.milcord.com/blog.