Like the death of distance argument. Digital networks actually raised the rewards to agglomeration by more than they killed distance costs. A mobile phone is more useful in NYC than it is in Dakota, though it is useful in both places.

Agreed 100%

“Although theory may not be as prominent as it once was, it remains essential for understanding the (increasingly) complex world we live in. One cannot analyze the bewildering amount of data now available without the organizing framework that theory provides. I would also suggest that one cannot understand the extraordinary events that we have recently witnessed, such as the financial crisis, or make sensible policy recommendations in response to these events, without the organizing framework of theory.”

Sounds just like when people play MMOs

Nice.

Although I read a paper this morning about how grade inflation could be good for everyone. It’s forthcoming in the AEJ:micro, so passed some solid peer review. Apparently the authors felt no need to test their hypothesis against any data or reality. Rather, reality just helped motivate them to construct a fantasy world for us all to gambol playfully around in.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2274210

But defining the analytic and quantitative content, and the way they inter-relate and are formed (eg the meta concepts of what is and categorization, which qualitative work helps with), is still a useful way to understand what a discipline has to offer. The person being quoted may merely be saying that, from their perspective, there is too little data analysis in economics. Of course this implies that they know virtually nothing about how economics is actually done, but it is still a perspective 😉

I’m confused. Are you saying economics is not a science because economists can’t run controlled experiments to reliably reproduce their results. So they’re just data analysts and that’s the same as a data scientist? Because that sounds… controversial!

I guess what I am trying to say in far too many words and should have said at the start is that the term ‘data scientist’ is a title given to data analyists or statisticians who want to pretend they are scientisits. Seeing as economists are already keen data analysts the quote above about econonomists being replaced by data scientists is moot.

What you say makes sense, although I would strongly differentiate between what I’ve termed a data scientist (in my head at least) and what I would consider a “scientist”, as scientific method requires reproductivity (not something a fair chunck of data can allow for, nor the point of quite a bit of analysis). Perhaps the term Data Analyst is more appropirate? Agreed that big data is not the panacea many claim it to be but could be useful in many instances.