We’ll return to Jaron Lanier’s article The Serfdom of Crowds in the February Harper’s Magazine that Spot mentioned yesterday. The thrust of Lanier’s argument presented in the article:
At the time the Web was born, in the early 1990s, a popular trope was that a new generation of teenagers, reared in the conservative Reagan years, had turned out to be exceptionally bland. The members of “Generation X” were characterized as blank and inert. The anthropologist Steve Barnett saw in them the phenomenon of pattern exhaustion, in which a culture runs out of variations of traditional designs in their pottery and becomes less creative. A common rationalization in the fledgling world of digital culture back then was that we were entering a transitional lull before a creative storm—or were already in the eye of one. But we were not passing through a momentary calm. We had, rather, entered a persistent somnolence, and I have come to believe that we will escape it only when we kill the hive. [at page 19]
Lanier says that the “digital hive” affects the way we think:
People degrade themselves all the time in order to make machines seem smart. Before the 2008 stock-market crash, bankers believed in supposedly intelligent algorithms that could calculate credit risks before the bank makes bad loans; we ask teachers to teach to standardized tests so a student will look good to an algorithm. We have repeatedly demonstrated our species’ bottomless ability to lower our standards to make information technology good, but every manifestation of intelligence in a machine is ambiguous. The same ambiguity that motivated dubious academic artificial intelligence projects in the past has been repackaged as mass culture. . . . [at page 15]
High stakes tests in education are the apotheosis of the phenomenon. They are, to use Lanier’s terminology, evidence of a “persistent somnolence.” A numbing and dumbing ushered in by the conservative era but encouraged and elevated by a deference to the algorithm.
This is a system better prepared to produce thousands or millions of identical cans of tuna (until the tuna are gone, anyway) rather than an educated population capable of continuing cultural variation and creativity.
It isn’t only K-12 education that has suffered from numbskull hive thinking. Higher education, especially in fields like economics, has also taken a big hit. This is a bit from a 2008 New York Times interview with the economist Jamie Galbraith:
Do you find it odd that so few economists foresaw the current credit disaster? Some did. The person with the most serious claim for seeing it coming is Dean Baker, the Washington economist. I saw it coming in general terms.
But there are at least 15,000 professional economists in this country, and you’re saying only two or three of them foresaw the mortgage crisis? Ten or 12 would be closer than two or three.
What does that say about the field of economics, which claims to be a science? It’s an enormous blot on the reputation of the profession. There are thousands of economists. Most of them teach. And most of them teach a theoretical framework that has been shown to be fundamentally useless. [italics are Spot's]
You’re referring to the Washington-based conservative philosophy that rejects government regulation in favor of free-market worship? Reagan’s economists worshiped the market, but Bush didn’t worship the market. Bush simply turned over regulatory authority to his friends. It enabled all the shady operators and card sharks in the system to come to dominate how we finance.
An entire generation of economics teachers and financiers have elevated the model of the economy to the point where they think it is the economy, and the economists have taught it to their students. Famously, to Spot, anyway, Ton Winkret (“NBBooks” at Kos) told a story during his appearance at Drinking Liberally about a noted economist — who didn’t understand that information underrepresents reality — who said that the economic bubble wouldn’t burst because he “didn’t see it coming out of his models.”
The whole Winkret video is worth watching, because it is full of examples of this kind of thinking at work.
Lanier calls his book a “manifesto,” urging that we’ll have to work hard to avoid confusing information and the algorithm with reality.