What do stock trading and conflict early warning systems have in common? Interestingly, both rely heavily on mathematical patterns of recognition. According to Joseph Bock, Director of Graduate Studies at the Eck Institute of Global Health at the University of Notre Dame, scholars such as Phil Schrodt have been applying the mathematics of stock trading to detect and identify conflict before it happens. This pattern recognition is part of a process that enables local citizens, NGOs, and humanitarian workers to use cell phones, radio, and online forums to help detect and prevent religious, ethnic, and politically motivated violence. A few weeks ago, Prof. Bock came to the World Bank to talk about his new book, The Technology of Nonviolence, where he discussed the use of social media and other forms of technology to both detect and respond to outbreaks of deadly conflict.
What struck most about the presentation were the methodology and analysis used to understand conflict early warning systems and technology. When contemplating how a text message can be used track a violent protest in Kenya, I had a tendency to think in more spontaneous terms. But as I took on the role of a day trader, at the prompting of Prof. Bock, I was impressed with the systems and indicators that can be combined with various technologies to identify patterns in a conflict.
The presentation was quite fascinating and covered background information, case studies, critical questions, common myths, and ethical imperatives surrounding conflict early warning systems.
To begin with, Prof. Block provided an analytical framework to guide our understanding of how violence unfolds. This framework, which is based on the work of various authors, was divided into three components: underlying conditions, consensus building towards violence, and the process during a lull. For the first component, a precipitating event such as a hostile situation characterizes the underlying event. In the consensus building process, there are indicators that help forecast different types of violence, including riots, gang violence, and genocide. These indicators can take the form of a rumor, which can help determine the amount of time needed to intervene before a conflict. Finally, during the lull, there is a time period that can be marked by threats and other types of emotional engagement.
The analytical framework helped set the stage for the case studies, which consisted of examples from India, Ethiopia, the United States, Sri Lanka, and Haiti. Among these, I found the most interesting case to be in India, where an organization called St. Xavier Social Services Society worked with women’s groups in the slums of Ahmedabad to help organize and prevent ethnoreligious violence with the use of text messages. Their strategy was to develop networks of community organizers with cell phones and the main indicator they used were rumors. When they got a sense of a brewing event, they would contact officials to seek protection. In the other case studies, there were several other examples where communities used crowd sourcing, digital mapping, and sophisticated events databases to prevent violence, but what appealed to me most about the India case was the simplicity of the technology and methods they used to respond to violence.
Previously, when I thought about conflict prevention and early warning systems, I tended to think about grand, elaborate, and expensive schemes --- one of the common myths that Prof. Bock effectively dispelled. But the presentation proved that there are simple processes, technologies, training, and ethical guidelines that can help communities recognize instances of violence and play their role in keeping the peace.
Photo credit: The World Bank