Improvements in Conflict Forecasting? (Updated Post)

I exchanged several emails with Professor Jack Goldstone today regarding an article of his (co-authored with seven other professors) currently under review: “A Global Model for Forecasting Political Instability.” I got in touch with Jack after a reference in David Nyheim‘s recent report on early warning caught my eye. Namely that conflict forecasting tools were now producing 80% accurate forecasts.

Jack did confirm the reference. Most of the Political Instability Task Force (PITF) models now have an 80%+ accuracy rate vis-a-vis forecasting state crises 2 years in advance. “There is a global model which averages 81.6% accuracy across 3 random control sets for both event and non-event forecasting, and a sub-Saharan Africa model that averages about 85%.” The article is still under review, so for now the piece should be referenced as “Working Paper, Center for Global Policy, George Mason University.”

Jack kindly shared the abstract with me:

Applying case-control methodology to data from countries worldwide from 1955 to 2003, the authors develop a statistical model of political instability that distinguishes with roughly 80-percent accuracy between countries headed for new crises two years hence and those that will remain stable. The resulting model employs few of the variables championed by those who write on political instability and is comparatively simple, using only a few variables, all but one of them categorical. A measure of regime type emerges as the most powerful predictor of instability in the two-year time frame, leading the authors to conclude that political institutions, not economic conditions, demography, or geography, are the most important predictors of the onset of political instability.

I find it particularly interesting that regime type appears to be the most salient predictor of political instability. What are the implications for conflict early response and humanitarian intervention? When I asked Jack why he and his colleagues had not applied their model through to 2007/2008 in order to make predictions for 2008-2010, he mentioned that the organization sponsoring the research has exclusive rights to the data and predictions about the present and near future. This is unfortunate, but of course I understand that this is beyond the team’s control. But it does beg the following question: does closed, proprietary research on conflict forecasting models contribute to the warning-response gap?

Update: Professor Goldstone kindly shared additional information. While he and his research team cannot use the material, their sponsors are making extensive use of the forecasts. Indeed, Centcom is drawing on the conflict modeling and parts of the forecasts are also being incorporated into the OECD fragile states response planning. So while the funder wishes to control dispersal, they are not keeping it fully locked up.

It would be useful if we could identify which operational responses/interventions (either by Centcom or the OECD) can be traced back to decision-making processes that were directly influenced by information generated from PITF’s models.


One response to “Improvements in Conflict Forecasting? (Updated Post)

  1. It’s easier to work towards your own interests than working together as a group to solve a problem. It’s even harder to unite other countries with different global agendas towards a common goal.

    There’s a lot of thought provoking video clips regarding complexity and how global problems become almost impossible to solve on a Facebook community page

    Why do we have a tendency to fight one another when we know sharing results in the most optimum outcome for everyone? Why does our biology cause us to hurt the ones we love, hoard resources and compete with one another?

    Heres the link to the video

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