Template:M intro design how the laws of data science lie
When a man throws a ball high in the air and catches it again, he behaves as if he had solved a set of differential equations in predicting the trajectory of the ball. He may neither know nor care what a differential equation is, but this does not affect his skill with the ball. At some subconscious level, something functionally equivalent to the mathematical calculations is going on.
Really powerful explanatory laws of the sort found in theoretical physics do not state the truth.
In 1983 philosopher Nancy Cartwright wrote the seemingly scurrilous How the Laws of Physics Lie — not quite the post-modernist tripe it sounds, but rather a serious and literate work of analytical philosophy. Cartwright’s point was that scientific laws are formulated in conditions so rigid, isolated and controlled that, even though they might be perfectly valid within those conditions, they are practically useless “in the real world,” where those conditions have no hope of existing. So the principles of Newton’s mechanics, assuming as they do no inconveniently intervening forces like friction, gravity, inelasticity, might plat the trajectory of an object on a graph, but have no chance of plotting the trajectory of the proverbial crisp packet blowing across St Mark’s Square. You will spend a lot of time with a slide rule and an anemometer; when you look up the packet will be gone.
The same observation animates Gerd Gigerenzer’s faith in heuristics over science: despite Richard Dawkins’ trite conviction to the contrary, a fielder performs no differential equations on the way to catching a flying cricket ball.
We trick ourselves into believing the power of our scientific laws, wilfully blind to
We have a sense a similar thing may be true of data science.
- ↑ The Selfish Gene, 2nd Ed., 95 — see it on Dawkins’ website.