Comment by 001sky | Hacker News Reader

Comment by 001sky | Hacker News Reader

"Simple models and a lot of data trump more elaborate models based on less data..."

If we make the leap and assume that this insight can be at least partially extended to fields beyond natural language processing, what we can expect is a situation in which domain knowledge is increasingly trumped by "mere" data-mining skills.

This is a great point. For many years, domain knowlege was merely such experience ex ante. In otherwords, the barrier to domain knowlege was access to data. In lieu of this, perhaps theory to estimate it. As data becomes larger, more free, and more amenable to analysis by a larger group of talent the "domain knowledge" itself as a barrier to entry (prestige, effectiveness) seemingly declines. Whle this is in theory good (more access, more analysis), there is probably a corralary that we should expect turf-wars and restriction to access, as those previously in positions of privledge fight to retain their status as "keepers of the keys".

I've spent some time in the economics field and certainly found that getting access to quality data was an enormous challenge. In fact a good amount of the work is spent putting together data sets. This is probably the bulk of what econ grad students do.

Besides this, it's important to recognize the difference between identifying correlations and patterns in data vs understanding the mechanisms behind the phenomena. Krugman makes a strong point: "The problem is that there is no alternative to models. We all think in simplified models, all the time. The sophisticated thing to do is not to pretend to stop, but to be self-conscious -- to be aware that your models are maps rather than reality." [1]

Data-mining will help generate and support hypotheses, but this is complementary to model building.

[1] http://web.mit.edu/krugman/www/dishpan.html

Econ is an interesting subject. Basicaly, it's taught using slide-ruler-y math from the 1950's[1]. Its amazing that learning to build linear algebra models without a slide-ruler (ie, by hand), comined witha year or two of statistics and some calc, makes someone an expert in "Economics".

If you think about the huge increase in computer power available today, it seems a field ripe for disruption. However, the guys that run the fed, and guys like Krugman are basically from the "slide rule era" of Econ.

I'm sympathetic to the power of modeling and techniques, but I do think its a case of the more you know, the more you understand what you don't know. And I think for most people the deeper you go into the field of Econ, the more this becomes apparent. Its getting better, though, and I'm sure in another generation (once the current tenures expire) things will look a good deal different (hopefully better).

Not sure if your views are similar, though.

[1] You see no little to no respect for dynamic systems that are chaotic or non linear; bounded rationality and its antecedent effcts; the role of institution underpinnings to markets, etc. Just to name a couple that are glaringly relevant and empirically important, but not subject to "hand math".

you should actually make an effort to learn and understand modern economics before coming to such strong conclusions. there are good reasons why the discipline exists in its current form.

it is clear from the "slide-ruler-y math" jibe that you have no idea about the technical ability required to do research, especially in theoretical microeconomics and econometrics. you'll need more than mastery of "Mathematics for Engineering" and MATLAB to disrupt the economics profession.

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