Our little meal consists of only two courses and you have to grab your own apéritif but I think you might like it.
A balloonist, lost, sees someone walking down a country lane. The balloonist lowers the balloon and shouts down to the the walker:
— Where am I?
— About 20 feet above the ground, comes the reply.
After a moment’s pondering, the balloonist says:
— You must be an economist.
— How did you know?
— Your information is perfectly correct — and totally useless.Le Plat Principal
Critique Économique par Noah Smith
Economics Has a Math Problem
A lot of people complain about the math in economics. Economists tend to quietly dismiss such complaints as the sour-grapes protests of literary types who lack the talent or training to hack their way through systems of equations. But it isn't just the mathematically illiterate who grouse. New York University economist Paul Romer -- hardly a lightweight when it comes to equations -- recently complained about how economists use math as a tool of rhetoric instead of a tool to understand the world.
Personally, I think that what’s odd about econ isn’t that it uses lots of math -- it’s the way it uses math. In most applied math disciplines -- computational biology, fluid dynamics, quantitative finance -- mathematical theories are always tied to the evidence. If a theory hasn’t been tested, it’s treated as pure conjecture.
Not so in econ. Traditionally, economists have put the facts in a subordinate role and theory in the driver’s seat. Plausible-sounding theories are believed to be true unless proven false, while empirical facts are often dismissed if they don’t make sense in the context of leading theories. This isn’t a problem with math -- it was just as true back when economics theories were written out in long literary volumes. Econ developed as a form of philosophy and then added math later, becoming basically a form of mathematical philosophy.
In other words, econ is now a rogue branch of applied math. Developed without access to good data, it evolved different scientific values and conventions. But this is changing fast, as information technology and the computer revolution have furnished economists with mountains of data. As a result, empirical analysis is coming to dominate econ.
One sign of this is the sudden burst of interest in machine learning in the economics field. Machine learning is a broad term for a collection of statistical data analysis techniques that identify key features of the data without committing to a theory. To use an old adage, machine learning “lets the data speak.” In the age of Big Data, machine learning is a hot field in the technology business, and is a key tool of the rapidly expanding field of data science. Now, econ is catching the bug.
Two economists who have been pushing for the adoption of machine learning techniques in economics are Susan Athey and Guido Imbens of Stanford University. The two economists explained machine learning techniques to an interested crowd at a recent meeting of the National Bureau of Economic Research. Their overview stated that machine learning techniques emphasized causality less than traditional economic statistical techniques, or what's usually known as econometrics. In other words, machine learning is more about forecasting than about understanding the effects of policy.
That would make the techniques less interesting to many economists, who are usually more concerned about giving policy recommendations than in making forecasts....MOREBravo!
