Conflict simulation, peacebuilding, and development

The Economist: The Science of Civil War

The latest issue of The Economist (21 April 2012) includes an article on advances in computational modelling of civil war and insurgency:

FOR the past decade or so, generals commanding the world’s most advanced armies have been able to rely on accurate forecasts of the outcomes of conventional battles. Given data on weather and terrain, and the combatants’ numbers, weaponry, positions, training and level of morale, computer programs such as the Tactical Numerical Deterministic Model, designed by the Dupuy Institute in Washington, DC, can predict who will win, how quickly and with how many casualties.

Guerrilla warfare, however, is harder to model than open battle of this sort, and the civil insurrection that often precedes it is harder still. Which, from the generals’ point of view, is a pity, because such conflict is the dominant form of strife these days. The reason for the difficulty is that the fuel of popular uprisings is not hardware, but social factors of a type that computer programmers find it difficult to capture in their algorithms. Analysing the emotional temperature of postings on Facebook and Twitter, or the telephone traffic between groups of villages, is always going to be a harder task than analysing physics-based data like a tank’s firing range or an army’s stocks of ammunition and fuel.

Harder, but not impossible. For in the war-games rooms and think-tanks of the rich world’s military powers, bright minds are working on the problem of how to model insurrection and irregular warfare. Slowly but surely they are succeeding, and in the process they are helping politicians and armies to a better understanding of the nature of rebellion.

I think the article, however, overstates the state-of-the-art, however. We’re a very, very long way from having computational models that tell use very useful policy- or warfighting-things about actual civil wars. Indeed, despite the title and introduction, the piece in The Economist largely focuses on much more narrow applications of analysis and technology.

Moreover, the article rather glosses over what seems to me to be an increasingly serious problem in this field: the gap between producers of data analysis and prediction, and users. Remarkably few professional conflict analysts that I’ve met—a category into which I would put the intelligence community, diplomats, and that part of the development community that works in fragile and conflict-affected countries—finds such modelling very useful in an operational sense. On the contrary, many dismiss it out of hand, even when it raises interesting questions.

Conversely, the researchers working in the area tend to over-sell what they have actually achieved. In so doing only further alienate those who supposed to find the analysis of practical use.

In short (as I recently suggested in a comment on models that attempt to provide early warning of genocide), far more attention needs to be devoted to “interface” issues of how the insights of technical modelling and the needs of conflict analysts can better be better matched.

2 responses to “The Economist: The Science of Civil War

  1. seachangesimulations 25/04/2012 at 4:44 am

    > more attention needs to be devoted to “interface” issues of how the insights of technical modelling and the needs of conflict analysts can better be better matched.

    Excellent, excellent point. There is great room for improvement here.

  2. brtrain 26/04/2012 at 6:55 pm

    Agreed, agreed as always.
    I can understand where the mania for precise predictive power in models comes from, but I don’t see how it sustains itself. A moment’s thought would tell anyone that studying the past (which is the only way to validate a model) is a way to learn about the future, but that it’s only an imperfect way to illuminate only part of what that future could be. It’s like expecting the sun to rise every day at 6:47 precisely, and then dismissing the concept of wristwatches when it doesn’t.
    As humans, we seem always to be searching for patterns in life and experieince, which leads us to want to model everything in sight. But we don’t always stop to think about the value of the model or analogy or observation to others than the ones who built the model. This is indeed where we fall down (and I am falling down in that I can’t see what I’m typing right now!)

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