Posts Tagged ‘eliot’

Sometime last century, previously qualitative subjects were injected with hearty doses of empiricism. The advances from these new approaches swept across disciplines as diverse as finance to media studies. Today, quantitative grounding is considered a requisite for academic acceptability.

But what are the implications of this empiricism?

In his great history of academic economics and finance, The Myth of the Rational Market, Justin Fox follows how those disciplines became enamored with quantifying everything. Formulas, models, data, math. These were the approaches and tools taken seriously. In the process, phenomenon that weren’t quantifiable got tossed out – humans became rational actors, and it took monumental efforts by the behavioral economists to begin to re-imagine man in a more accurate, nuanced light. Yet, the damage was done; unintentionally, and not without adding great insights, but nonetheless done.

I wonder if the current trend towards huge data sets and massive computational power will have similar unintended consequences. There’s no lack of pessimists who think the Internet is ruining human society, but people like Jaron Lanier rarely hit the target and tend to be sensationalists trying to sell books. Of course, they’re up against plenty of Pollyanna’s who are selling this all as the greatest thing since sliced bread.

I think that whatever is happening, and whatever negatives there may be, is far less exciting than cover stories for The Atlantic or new business opportunities for Silicon Valley.

In a new special report on the data deluge, The Economist generally misses this theme. This isn’t to say the report isn’t quite good (it is) or that I would expect them to cover this (I don’t). However, one of the articles does get close when examining how to handle the extraordinary amount of information and the infrastructure needed to deal with it:

The cornucopia of data now available is a resource, similar to other resources in the world and even to technology itself. On their own, resources and technologies are neither good nor bad; it depends on how they are used. In the age of big data, computers will be monitoring more things, making more decisions and even automatically improving their own processes—and man will be left with the same challenges he has always faced. As T.S. Eliot asked: “Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”

Two months ago, I would have agreed that “technologies are neither good nor bad,” but a course on infrastructure studies has definitely made me question that. Eliot’s quote, though, is the right question to be asking.

Update: I forgot to include another relevant bit:

Processing data is another concern. Ian Ayres, an economist and lawyer at Yale University and the author of “Super-Crunchers”, a book about computer algorithms replacing human intuition, frets about the legal implications of using statistical correlations. Rebecca Goldin, a mathematician at George Mason University, goes further: she worries about the “ethics of super-crunching”. For example, racial discrimination against an applicant for a bank loan is illegal. But what if a computer model factors in the educational level of the applicant’s mother, which in America is strongly correlated with race? And what if computers, just as they can predict an individual’s susceptibility to a disease from other bits of information, can predict his predisposition to committing a crime?

Update 2: I have previously written a bit more extensively about my reservations about quantification here.