Data Exhaust and Informational Efficiency
Heard an interesting talk by Paul Kedrosky a few weeks ago at PARC titled Data Exhaust, Ladders, and Search.
The gist of the talk is that human behaviors of all kinds leave traces which constitute latent datasets about that activity. Social scientists have long had a name for gathering this type of data: unobtrusive observation. Perhaps the most famous example is looking at carpet wear in a museum as a way of figuring out which exhibits captured the most visitor attention or garbology and related “trace measures used by anthropologist W. Rathje in the 70s and 80s.
One of Kedrosky’s nicer examples was comparing aerial view of Wimbledon center court at the end of a recent tournament with one from the 1970s. The total disappearance of the net game from professional tennis was clearly visible in the wear patterns on the grass court.
In addition to a number of neat examples (ladders found on highways as indicator of housing bubble was a favorite) of using various techniques to capture “data exhaust” (indeed, he suggests, it’s the entire principle behind google), he asks the question: What are the consequences of an instrumented planet? That is, a planet on which more and more data exhaust is captured and analyzed, permitting better decisions and more efficient choices.
In fact, one of the comments on Kedrosky’s blog post about the talk (by one J Slack) suggests a continuing move toward “informational efficiency” — with more and more instrumentation generating data and more and more connectivity, he suggests, “we’ll be continuously approaching an asymptotic efficiency, though never quite getting there.”
A standard definition of informational efficiency is “the speed and accuracy with which prices reflect new information” (TheFreeDictionary.com). But there is some circularity here — in this context it’s only information if it does affect the price, otherwise it’s mere noise. And so we’re still left with the challenge of sorting out the signal from the noise even after the data has been extracted from the exhaust. And the more of everything the more of a job it is.
Bottom line: I think “data exhaust” is a great concept, but I don’t think perfecting its capture and analysis gets you to a fully efficient use of information about the world (even asymptotically). The second law of thermodynamics kicks in along the way for starters, but the boundedness of human cognition finishes the job.
Somebody is probably going to point out that evolution already does this (that is, it’s the most unobtrusive data collection method of all). But it takes big numbers and lots of time to do it and the result, though beautiful, is messy.
More to think about here, to be sure.
See Also (2014)
Johnson, Steven. “What a Hundred Million Calls to 311 Reveal About New York.” Wired Magazine 11.01.10
Silence on the Soc of Info Front
Week’s worth of silence here was due to a quick trip to Paris in the company of my partner, Gillian Hadfield, who was talking at a conference on new institutional economics. Sociologists spend a lot of time attacking their straw-person version of economics, but I find that I learn a whole lot from them, especially these smart institutional economists. Prediction: I’ll probably start investing some serious time in learning more game theory. That probably sounds like apostasy to some of my sociology colleagues, but so be it; I find a catholic approach to things intellectual, and a disregard for disciplinary boundaries to be the most fruitful approach to understanding the social world. It’s a hard nut to crack and we need all the tools we can find.
So, I’ll be back on the information trail soon.
Oh: Paris was great. What a city!