Bradley Perry, a colleague of mine who just completed his Masters of Science in Applied Intelligence, kindly shared a copy of his excellent MA thesis entitled “Fast and Frugal Conflict Early Warning in Sub-Saharan Africa: The Role of Intelligence Analysis.” Bradley carried out his study to counter the erroneous assumption that only those who have large budgets and operate outside high-conflict regions (e.g., academics) are best placed to engage in conflict early warning analysis.
Instead of drawing on dozens and dozens of indicators like the majority of early warning systems, which necessitates substantial amounts of data (most of which is highly aggregated and/or of poor quality), Bradley takes just three indicators to forecast conflict escalation: income inequality, ethnic fractionalization and political freedom. The results from this “good enough” model suggest that we should question our field’s inclination for data-intensive methodologies. In Bradley’s own words, “the results do argue that both the conflict early warning and intelligence communities should consider the value of fast and frugal analysis.”
The fact that the conflict early warning field has been riddled with data-intensive methodologies for the past 20 years is directly due to the fact that those designing these methodologies are for the most part hardcore academics obsessed with prediction and sophisticated econometric models. To be sure, “most conflict early warning systems rely on resource intensive methods. They often take years to develop, and are built on complicated algorithms that require vast amounts of data.” As Bradley adds, however:
“It is safe to say no one has created a system that has the ability to predict; it is likely that no one ever will. In fact, if prediction were the goal in conflict early warning, the intelligence field would have little to offer. Former US government intelligence analyst and author of Anticipating Surprise: Analysis for Strategic Warning, Cynthia Grabo gives this caveat: ‘Warning is not a fact, a tangible substance, a certainty, or a provable hypothesis. It is not something which the finest collection system should be expected to produce full blown or something which can be delivered to the policymaker with the statement, ‘Here it is. We have it now.’”
Below are excerpts from Bradley’s research that strongly resonated with my experience in the field of conflict early warning:
William G. Nhara, a former advocate for the establishment of an early warning system for the OAU, suggests that an early warning system for the African context should be based on a number of methodologies; rather than detail how their incorporation into one system might appear however, he merely lists general sources of information to include: historical surveys and analyses of events, analyses of the content of documents and reports, comparative analyses of relevant information, physical inspections and field visits, statistical sampling and inference, operations research techniques, economic and econometric analysis, and modeling and remote sensing. This enumeration offers little explanation as to how the analyst might process the information, except to say that the responsible agency should store it in a database.
Robert Mudida, a professor of International Conflict Management at the University of Nairobi, described the AU’s “Situation Room” as merely one set up with CNN TV. According to him, it is not proving to be an effective institution in regards to prediction (R. Mudida, pers. comm.).
Indigenous organizations, those with the most responsibility and the greatest chance for success in conflict early warning, are spending precious, yet scant, resources in research, development, and implementation of these models. However, if it is accurately feasible to avoid the complex set of indicators that accompany most warning models and skip altogether the danger of having otherwise accurate systems fall short in applicability, then the identification of a “good enough” model is worth pursuing.