Thursday, December 6, 2012

Considering the Obvious

Duncan Watts is a principal researcher at Microsoft and former professor of sociology at Columbia who is interested in what we can learn about humans from our networking behavior. I'm looking forward to reading his book "Everything is Obvious* *Once You Know the Answer" about common sense and its, well, weaknesses. His work has implications for marketing, social science research, and social services.

One example is government - we think we can use common sense, Watts says, to solve large social problems.
The problem with common sense is not that it isn’t sensible, but that what is sensible turns out to depend on lots of other features of the situation. And in general, it’s impossible to know which of these many potential features are relevant until after the fact (a fundamental problem that philosophers and cognitive scientists call the “frame problem”).
Nevertheless, once we do know the answer, it is almost always possible to pick and choose from our wide selection of common-sense statements about the world to produce something that sounds likely to be true. And because we only ever have to account for one outcome at a time (because we can ignore the “counterfactuals,” things that could’ve happened, but didn’t), it is always possible to construct an account of what did happen that not only makes sense, but also sounds like a causal story.
...
Common sense, in other words, is extremely good at making the world seem sensible, quickly classifying believable information as old news, rejecting explanations that don’t coincide with experience, and ignoring counterfactuals. Viewed this way, common sense starts to seem less like a way to understand the world, than a way to survive without having to understand it.
 Here's another interesting Watts column, about making predictions.
At the end of the day, making the right prediction is just as important as getting the prediction right, but it is only at the end of the day that know which prediction was the right prediction.
If this sounds hopeless, it is -- but only if we aspire to a level of certainty about the future that is at odds with the fundamental randomness of the world.  If we acknowledge that randomness, there are still useful predictions we can make, just as poker players who count cards can make useful predictions without ever knowing with 100% confidence which particular card is going to show up next.

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