Big Sky Cairn

If you’ve been reading for awhile, you are probably aware that I am broadly interested in scientific method - especially with regard to its application in the Catastrophic Global Climate Change debate. One of my biggest concerns with modern dogma of science is that matters of faith are conflated with matters of science. Unfalsifiable models - models that can be used to explain all possible data - are proposed as science and expected to receive the same protected position in discussions. I have argued that doing so threatens to dull the scientific scalpel.

An article in Wired is currently getting a lot of attention around the Internets. It declares the end of scientific method. It’s worth noting, of course, that Wired is not a scientific magazine, but a new-media rag that is more likely to overestimate the importance of new technologies in replacing the old.

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.

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But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. Consider physics: Newtonian models were crude approximations of the truth (wrong at the atomic level, but still useful). A hundred years ago, statistically based quantum mechanics offered a better picture — but quantum mechanics is yet another model, and as such it, too, is flawed, no doubt a caricature of a more complex underlying reality. The reason physics has drifted into theoretical speculation about n-dimensional grand unified models over the past few decades (the “beautiful story” phase of a discipline starved of data) is that we don’t know how to run the experiments that would falsify the hypotheses — the energies are too high, the accelerators too expensive, and so on.

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.

In short, the more we learn about biology, the further we find ourselves from a model that can explain it.

There is now a better way. Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

I’m certainly excited about the new frontiers that are being opened via petabyte computing. But I think it’s premature to think that we can throw the old methods out with the bathwater. The scientific method itself has advantages that cannot be duplicated by “the cloud” of data. Some of those advantages are explored in a rebuttal article at The Art of Technology.

Correlations are a way of catching a scientist’s attention, but the models and mechanisms that explain them are how we make the predictions that not only advance science, but generate practical applications.

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Overall, the foundation of the argument for a replacement for science is correct: the data cloud is changing science, and leaving us in many cases with a Google-level understanding of the connections between things. Where Anderson stumbles is in his conclusions about what this means for science. The fact is that we couldn’t have even reached this Google-level understanding without the models and mechanisms that he suggests are doomed to irrelevance. But, more importantly, nobody, including Anderson himself if he had thought about it, should be happy with stopping at this level of understanding of the natural world.

Go read the whole thing.