This one gives a much better explanation, while still being accessible: http://www.hpcwire.com/features/IBM-Invents-Short-Cut-to-Ass...
Roughly speaking, when you have a lot of uncertainty on a lot of random variables it's too hard to propagate the uncertainty through the entire model. The standard approach is to just run 'enough' iterations of a monte carlo simulation - while IBM seems to have come up with a more efficient solution.