An excellent piece that chimes with my thinking! The challenge of too much data is real more than ever, and without good theories or models to make sense out of them, they may turn into a burden rather than resources. They key is to go universals and then going down to parts, which is exactly what I have been doing in my ecological research approach. This I would call a theoretical economic of scale. Very interesting story on DNA double helix and planets orbiting the sun in non perfect circles!
In 1963, Bernard K. Forscher of the Mayo Clinic complained in a now famous letter printed in the prestigious journal Science that scientists were generating too many facts. Titled Chaos in the Brickyard, the letter warned that the new generation of scientists was too busy churning out bricks — facts — without regard to how they go together. Brickmaking, Forscher feared, had become an end in itself.
. … It became difficult to find the proper bricks for a task because one had to hunt among so many. …
There are three basic reasons scientific data has increased to the point that the brickyard metaphor now looks 19th century. First, the economics of deletion have changed. Now, it’s often less expensive to store them all on our hard drive (or at some website) than it is to weed through them.
Second, the economics of sharing have changed. The ability to access and share over the Net further enhances the new economics of deletion; data that otherwise would not have been worth storing have new potential value because people can find and share them.
Third, computers have become exponentially smarter.
The result of having access to all this data is a new science that is able to study not just “the characteristics of isolated parts of a cell or organism” (to quote Kitano) but properties that don’t show up at the parts level.
There are many fewer universals than particulars, and you can often figure out the particulars if you know the universals: If you know the universal theorems that explain the orbits of planets, you can figure out where Mars will be in the sky on any particular day on Earth.
Aiming at universals is a simplifying tactic within our broader traditional strategy for dealing with a world that is too big to know by reducing knowledge to what our brains and our technology enable us to deal with.
We therefore stared at tables of numbers until their simple patterns became obvious to us. Johannes Kepler examined the star charts carefully constructed by his boss, Tycho Brahe, until he realized in 1605 that if the planets orbit the Sun in ellipses rather than perfect circles, it all makes simple sense. Three hundred fifty years later, James Watson and Francis Crick stared at x-rays of DNA until they realized that if the molecule were a double helix, the data about the distances among its atoms made simple sense.
With these discoveries, the data went from being confoundingly random to revealing an order that we understand: Oh, the orbits are elliptical! Oh, the molecule is a double helix!
With the new database-based science, there is often no moment when the complex becomes simple enough for us to understand it. The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges.