The financial crisis has given rise to a series of catastrophes related to mathematical modeling?. A common response to these problems is to call for those models to be revamped, to add features that will cover previously unforeseen issues, and generally speaking, to make them more complex.
For a person like myself, who gets paid to “fix the model,” it’s tempting to do just that, to assume the role of the hero who is going to set everything right with a few brilliant ideas and some excellent training data.
Unfortunately, reality is staring me in the face, and it’s telling me that we don’t need more complicated models.
If I go to the trouble of fixing up a model,� say by adding counterparty risk considerations, then I’m implicitly assuming the problem with the existing models is that they’re being used honestly but aren’t mathematically up to the task.
But this is far from the case – most of the really enormous failures of models are explained by people lying.� Before I give three examples of “big models failing because someone is lying” phenomenon, let me add one more important thing.
Namely, if we replace okay models with more complicated models, as many people are suggesting we do, without first addressing the lying problem, it will only allow people to lie even more. This is because the complexity of a model itself is an obstacle to understanding its results, and more complex models allow more manipulation. via .