… Fit the Battle of Econo, Econo, Econo
In All Quants Need Informative Data, I pointed out what some physics problems and economics problems have in common: they have only uninformative data (and need to transform the few data they get into model parameters - by constant recalibration).
This is a blog post of the economist Chris House: Why Are Physicists Drawn to Economics.
And this are the replies from Mark Buchanan: Arrogant physicists - do they think economy is easy? and Noah Smith: Coming into econ from physics (and other fields).
I think, all agree that the problem is "complexity" (in my understanding, a system is complex if it contains at least one subsystem of co-evolution).
But it seems economists over estimate the difference between physics and economics intuition - system behavior versus human behavior - and the physicist's emphasis on lab experiments (the data problem).
Brian Arthur's complexity economics as a different frame work for economic thought seems to be motivated by physics intuition (simplified: positive feed back structures drive emergent systems; market dominance drives market dominance, innovation drives innovation, ...). In this framework equilibrium (neoclassical) economics is a special case of complexity economics (not the other way around). This is the article.
The difference between system behavior and human behavior? IMO, human behavior changes when humans use models intensively.
In quant finance: up to 1987 when all option traders played the simple Black Scholes game (constant volatility) everything worked well - the headache began with the introduction of the far out of the money options and the exploration of the volatility smile … now models have adapted, but there is still the problem of uninformative data.
And this is, IMO, the kind of meso-layer (remember the discussion about material behavior) between the micro layer of concrete economic transformations and the macro layer of the development of a complete economy. Understand the influence of game rules and derivative economic objects.
You can't predict future but build it.
Still a lot to do for quants: take models from good to great and solve them adequately, extract informative data, calibrate, recalibrate (or even co-calibrate) … all blazing fast.
Picture from sehfelder