On Big Data, Economics, Science, and Predictions

EconomicsPredictions

 

“…many economists don’t seem troubled when they make predictions that go wrong. Readers of Paul Krugman and other like-minded commentators are familiar with their repeated complaints about the refusal of economists to revise their theories in the face of recalcitrant facts. Philosophers of science are puzzled by the same question. What is economics up to if it isn’t interested enough in predictive success to adjust its theories the way a science does when its predictions go wrong?…

For the foreseeable future economic theory should be understood more on the model of music theory than Newtonian theory. The Fed chairman must, like a first violinist tuning the orchestra, have the rare ear to fine-tune complexity (probably a Keynesian ability to fine-tune at that). Like musicians’, economists’ expertise is still a matter of craft. They must avoid the hubris of thinking their theory is perfectly suited to the task, while employing it wisely enough to produce some harmony amid the cacophony”–Alex Rosenberg and Tyler Curtain, “What is Economics Good For?The New York Times, August 24, 2013

“The excitement about ‘big data’ in tech circles is very optimistic and many companies are rushing to hire ‘Data Scientists’ to profit from the explosion of hype about the reams of data collected inside their own organizations, and in the world outside.

But having access to big data doesn’t guarantee that companies, or individuals, will understand or be able to derive much value from it. The very few examples of companies doing that, are very few. And for a good reason – finding insight in all that data is difficult and becomes more difficult the bigger the data sets.

Take for example the field of economics — it’s the original big data profession. But in all these years, it hasn’t been able to do much at all. The profession is well-regarded and respected despite its collective failure to understand the economy and predict its behavior.

Surely, a big data profession such as the study of economics over the past 150 plus years would by now be refined and almost scientific in its precision, especially since these days we have as much compute power as an economist might need, not to mention even more data to analyze. But it’s not even close… Economics is easily the single most important failure of the application of Big Data. And to call economics the “dismal science” is unfair on scientists because there’s nothing scientific about it”–Tom Foremski, “Want proof of big data’s dismal failure? Look at economics,” memeburn.com, October 14, 2013

“The more exclusive economists became as a group, the more special grew their skills and the more formalized and abstract their theories, the surer became their hold on their chosen identity, for the society at large concurred with them in the idea of science as formal, quantitative, and inherently incomprehensible to layman… And the greater their authority as a science, the more often highly credentialed economists exchanged mathematical modeling for social preaching and parlayed their technical proficiency into positioned of generalized opinion leaders. Their influence grew in proportion to their remoteness; they were believed to be in possession of magical formulas and were expected to work magic… Faith is infinitely stronger than experience. Economists never fulfilled these expectations, but faith in their power never waivered”–Liah Greenfeld, The Spirit of Capitalism, 2001.

 

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About GilPress

I launched the Big Data conversation; writing, research, marketing services; https://whatsthebigdata.com/ & http://infostory.com/
This entry was posted in Big Data Backlash, Data Science, Predictions, Science. Bookmark the permalink.

2 Responses to On Big Data, Economics, Science, and Predictions

  1. Stephen Huppert says:

    An economist – someone who gets paid to make predictions and then gets paid to explain why the predictions were wrong.

    Like

  2. Pingback: On Big Data, Economics, Science, and Predictions | kwalitisme

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