“All models are wrong, but some are useful.”

So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now.

Today companies like Google, which have grown up in an era of massively abundant data, don’t have to settle for wrong models. Indeed, they don’t have to settle for models at all. (…) They are the children of the Petabyte Age. [A petabyte is equal to 1,000,000 gigabytes.]

The Petabyte is different because more is different. Kilobytes were stored on floppy disks. Megabytes were stored on hard disks. Terabytes were stored in disk arrays. Petabytes are stored in the cloud. We went from the folder analogy to the file cabinet analogy to the library analogy to — well, at petabytes we ran out of organizational analogies. (…)

It forces us to view data mathematically first and establish a context for it later. For instance, Google conquered the advertising world with nothing more than applied mathematics. It didn’t pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right.

Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required. That’s why Google can translate languages without actually “knowing” them. (…)

This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. (…) The big target here isn’t advertising, though. It’s science. The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years. (…)

But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. (…)

Now biology is heading in the same direction. The models we were taught in school about “dominant” and “recessive” genes steering a strictly Mendelian process have turned out to be an even greater simplification of reality than Newton’s laws. The discovery of gene-protein interactions and other aspects of epigenetics has challenged the view of DNA as destiny and even introduced evidence that environment can influence inheritable traits, something once considered a genetic impossibility.

{ Chris Anderson/Wired | Continue reading }

Anderson confuses statistical models with scientific ones. As far as the content goes, I’m completely unconvinced.

{ Seth Roberts | Continue reading }

related { Does human culture evolve via natural selection, as our genes do? }

READ MORE >> advertising, technology, google, robots | June 25th, 2008