When it comes to assigning blame for the current economic doldrums, the quants who build the complicated mathematic financial risk models, and the traders who rely on them, deserve their share of the blame. [See“A Formula For Economic Calamity” in the November 2011 issue]. But what if there were a way to come up with simpler models that perfectly reflected reality? And what if we had perfect financial data to plug into them?
Incredibly, even under those utterly unrealizable conditions, we'd still get bad predictions from models.
The reason is that current methods used to “calibrate” models often render them inaccurate.
That's what Jonathan Carter stumbled on in his study of geophysical models. Carter wanted to observe what happens to models when they're slightly flawed--that is, when they don't get the physics just right. But doing so required having a perfect model to establish a baseline. So Carter set up a model that described the conditions of a hypothetical oil field, and simply declared the model to perfectly represent what would happen in that field--since the field was hypothetical, he could take the physics to be whatever the model said it was. Then he had his perfect model generate three years of data of what would happen. This data then represented perfect data. So far so good.