Pragma Synesi – interesting bits

Compendium of interesting bits I come across, with an occasional IMHO

The End of Theory?

“All models are wrong, but some are useful” — I love that quote. For me it highlights the raison d’etre of science: to predict and therefore to increase control. I don’t agree with the article that theories and models will become obsolete, but it is time to add some new tools to the set of predicting tools we already have.  And use the most useful ones.



The End of Theory: The Data Deluge Makes the Scientific Method Obsolete

By Chris Anderson Email 06.23.08

“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.

Sixty years ago, digital computers made information readable. Twenty years ago, the Internet made it reachable. Ten years ago, the first search engine crawlers made it a single database. Now Google and like-minded companies are sifting through the most measured age in history, treating this massive corpus as a laboratory of the human condition. They are the children of the Petabyte Age.

The Petabyte Age 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. As we moved along that progression, 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.

At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. 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 (given equal corpus data, Google can translate Klingon into Farsi as easily as it can translate French into German). And why it can match ads to content without any knowledge or assumptions about the ads or the content.

Speaking at the O’Reilly Emerging Technology Conference this past March, Peter Norvig, Google’s research director, offered an update to George Box’s maxim: “All models are wrong, and increasingly you can succeed without them.”

This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.

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.

Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise.

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.

The best practical example of this is the shotgun gene sequencing by J. Craig Venter. Enabled by high-speed sequencers and supercomputers that statistically analyze the data they produce, Venter went from sequencing individual organisms to sequencing entire ecosystems. In 2003, he started sequencing much of the ocean, retracing the voyage of Captain Cook. And in 2005 he started sequencing the air. In the process, he discovered thousands of previously unknown species of bacteria and other life-forms.

If the words “discover a new species” call to mind Darwin and drawings of finches, you may be stuck in the old way of doing science. Venter can tell you almost nothing about the species he found. He doesn’t know what they look like, how they live, or much of anything else about their morphology. He doesn’t even have their entire genome. All he has is a statistical blip — a unique sequence that, being unlike any other sequence in the database, must represent a new species.

This sequence may correlate with other sequences that resemble those of species we do know more about. In that case, Venter can make some guesses about the animals — that they convert sunlight into energy in a particular way, or that they descended from a common ancestor. But besides that, he has no better model of this species than Google has of your MySpace page. It’s just data. By analyzing it with Google-quality computing resources, though, Venter has advanced biology more than anyone else of his generation.

This kind of thinking is poised to go mainstream. In February, the National Science Foundation announced the Cluster Exploratory, a program that funds research designed to run on a large-scale distributed computing platform developed by Google and IBM in conjunction with six pilot universities. The cluster will consist of 1,600 processors, several terabytes of memory, and hundreds of terabytes of storage, along with the software, including IBM’s Tivoli and open source versions of Google File System and MapReduce.1 Early CluE projects will include simulations of the brain and the nervous system and other biological research that lies somewhere between wetware and software.

Learning to use a “computer” of this scale may be challenging. But the opportunity is great: The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

There’s no reason to cling to our old ways. It’s time to ask: What can science learn from Google?

Chris Anderson ( is the editor in chief of Wired.


1This story originally stated that the cluster software would include the actual Google File System.



August 7, 2008 - Posted by | statistics | , , , ,


  1. Real scientists are never satisfied with the usefulness of crudely (statistics based) theory. Einstein WAS close with his Determinist detailed mechanical visualizing but he too fell to using statistical crudeness when he could have just changed his space curvature eq to G = R/3(v-squared)where R is the replacement sphere radius of the R-material point location in spacetime, G is the radius of the Higgs particl that defines the spin surface of R and v is the Higgs particle spped as antropic-seen going from one R to another adjaceng one next door. Mass as a count of surface defining G is numerically always equal to the square of three axis energy, 3(v-squared)! The simplied Eq is then a statement of Universal Harmony(UH) as least possible energy, friction, lowest possible energy state for every R location

    Comment by lfmorgan | August 8, 2008 | Reply

  2. I agree with you — any type of predictor, whether it’s theory, model, or stats-based, is wrong, hence there is a better one out there waiting to be discovered by those unsatisfied with the quality of predictions. But I still like the idea of using stats-based predictions when there is nothing better. It’s nice to have an additional tool besides math, and I think scientists should embrace it for its usefulness, but not exclusively as the article suggests.

    Comment by pragmasynesi | August 8, 2008 | Reply

  3. “Being statistically correct” cannot replace “being correct”. Or are we now satisified that our doctors can statistically heal us?

    And: not every company is Google. Actually, not many companies are like Google. Actually only a tiny minority of organisations are like Google. So, for the rest of us….

    And not only people lie. Data lies too. Statistical methods are prone to statistical manipulation. Mechanical models can have proofs “out-of-band”. Reproducable ones, because not everyone has access to Googles data store.

    I am not sure whether I like the “Googlification” of thinking.

    Comment by Robert Barta | August 9, 2008 | Reply

  4. I think you should give another look to this “Googlification”.
    Not as a replacement for existing scientific methods like the article suggests, but as an additional tool for cases where the old scientific method is not useful or practical for forecasting.
    Unfortunately in most cases there is no way to “correctly” predict outcomes. So yes, doctors are healing us statistically even now, because drug testing relies on statistics, and yes, there are problems galore with that (just look at drug recalls), but that’s the best tool we have for now. No foolproof reproduceable “mechanical” model exist, or is likely to be discovered any time soon.
    That’s where “googlification” could come in useful — cases which are not predictable right now with existing methods and tools (not as the article suggests, as a replacement for such tools). And with technology racing ahead at an exponential pace, with time more an more people will be able to make use of such tools.

    Comment by pragmasynesi | August 11, 2008 | Reply

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