Many thanks for your comment, I completely agree with you and also very much commend Bradley the arguments he makes in his thesis. Where could we get more information on your work in India?

Thanks again,
Patrick

As a final year Master student of Conflict Analysis and Peacebuilding, I find that ”Fast and Fugal” conflict early warning has a lot of substantial information as to how an efficient, yet simple, early warning system should be developed, using Sub-Saharan Africa as a test tube.

Resources on this field are indeed limited.

In India, Meta-Culture, an organisation dedicated to dialogue and conflict transformation is in the process of developing a conflict early warning system in Bangalore, and Bradley’s thesis has been a valuable source in the development of this system.

Thank you, Bradley.
Stacy-Ann

I’m very interested in the idea of “fast and frugal” early warning and am grateful for your passing along Bradley’s interesting thesis. I have a few reactions.

First, there are surely some data-intensive early warning efforts around, notably in Africa. But how widespread is the assumption that effective early warning requires these resource- and data-intensive models? To the contrary, I’d say that most policymakers (and probably most people in the field at large) discount the utility of statistical models for early warning. Gareth Evans’ comments on this are representative: “While a great deal of work has been done on early warning signs, particularly in the case of conflict generally, with some attempt to build complex quantitative models as well as lists of qualitative indicators, this enterprise remains for the most part an art rather than a science” (p. 74 of his new book, The Responsibility to Protect). He goes on to identify five factors he believes one should consider in making “these essentially seat of the pants judgments.” Bradley writes, “Most conflict early warning systems rely on resource intensive methods.” Yet most conflict early warning is not “systematized” but rather informal, relying primarily on qualitative analysis by country experts.

Second, the most rigorous statistical work done to develop a forecasting model has generated surprisingly simple results. I refer to the Political Instability Task Force and its global forecasting model of instability (see http://globalpolicy.gmu.edu/pitf/PITFglobal.pdf). Their effort has surely been resource- and data-intensive, and has included testing lots of complex models, but the results are neither particularly data-intensive nor complex. One could easily mistake the PITF membership as fitting Patrick’s description of “hardcore academics obsessed with prediction and sophisticated econometric models.” This makes their relatively simple results all the more noteworthy.

Third, depending on one’s measure of “good enough,” it may be possible to develop even faster and more frugal forecasting models. I recall a researcher telling me that using two variables (infant mortality and population size), one can get upwards of 70+% accuracy at forecasting episodes of mass killing. Many people have suggested that a few smart people in a room could forecast at least as accurately as any statistical model.

I’ve written previously that a risk assessment/early warning strategy should balance three attributes: (1) accuracy in estimating risk, (2) efficiency/feasibility of using the methods in question, and (3) perceived legitimacy of the process by key stakeholders. In the end, the best approach will depend on the context. So I fully support efforts to develop models that perform as accurately as extant ones, but with greater efficiency. Likewise, there is value in striving to improve the accuracy of our models, even if they become somewhat less efficient.

Lastly, I don’t think the biases of academics engaged as consultants are sufficient to explain data-heavy and resource intensive early warning systems in Africa—a region that one assumes should focus on efficiency. The politics of governments in the region are at least as important, I believe. For example, I’d hazard to guess that the ECOWARN system has 90+ indicators not because any academic would recommend so many, but because it was easier to forge political consensus by adding more and more indicators, instead of winnowing the list down to a few powerful ones.

Thanks again, Patrick.

Lawrence

Kris