There must be something about Dan Gardner that coerces me to read his topics in pairs. When I read Gardner’s last book, The Science of Fear, I immediately read Physics for Future Presidents as well, which had a fair amount in common.
Now Gardner’s latest book, Future Babble, is largely a sociological study, and what should I read immediately afterward but another sociology book, with no small amount of overlap. In fairness, Watts’ book ends up being the superior of the two.
When I was young, and first getting into classical music, I used to imagine that I was, say, the genius who wrote Rachmaninov’s Piano Concerto #3, instead of Sergei himself. I would then wonder, because I was occasionally precocious, if the work could still be a widely-respected and deeply-loved member of the classical canon if it were written by some American schmuck in the last years of the 20th century. Sadly, I reasoned, probably not.
This is also one of Watts’ central points, albeit made with the Mona Lisa rather than a concerto. Watts builds up his argument around the notion of “common sense”, a notion we all know but would find difficult to accurate define or quantify. He falls back and punts with Carl Taylor:
By common sense I mean the knowledge possessed by those who live in the midst and are a part of the social situations and processes which sociologists seek to understand. The term thus used may be synonymous with folk knowledge, or it may be the knowledge possessed by engineers, by the practical politicians, by those who gather and publish news, or by others who handle or work with and must interpret and predict the behavior or persons and groups.
Our common sense tells us that the Mona Lisa is a great painting because, as Watts puts it, “it has attributes X, Y, and Z. But really what we’re saying is that the Mona Lisa is famous because it’s more like the Mona Lisa than anything else.” What we perceive to be common sense—namely, that a famous piece of art is great—is really backformed from our knowledge that the art is already famous. At this point, the subtitle(?1) “Once You Know the Answer” should have an obvious meaning.
The same effect occurs in prediction-making, whence comes much of the overlap with Dan Gardner’s book. After an event has happened, it’s very easy to explain why… except that so often, this is never the explanation we would have given before it happened. Our common sense also tends to favor individual, dynamic actors, rather than an aggregation of low-level, systemic causes. In this, Watts politely savages Malcolm Gladwell’s The Tipping Point, which ascribed major changes to the activity of a few significant people enacting large change. In fairness, I believe Gladwell acceded the point that a few “influencers” alone could not enact change without an otherwise critical mass; Watts suggests, however, that these kinds of social network dynamics are nondeterministic, with predictions thwarted even by small random variations.
The problem, as Watts repeatedly points out, is that unlike repeatable experiments, the iconic stories which inform our common sense only happen once; we cannot rewind them and try them a different way to see if our hindsight explanation is the correct one. Is Apple successful because Steve Jobs is a dynamic leader and a visionary? Our common sense tells us it is, but we really don’t know for sure with any scientific certainty2.
Watts’ particular milieu, at least lately3 is the technological incarnations of those social networks—e.g. Facebook and Twitter, the latter of which he once used for a “Tipping Point”-style experiment. Sample size helps to illustrate Watt’s second big point, namely that no matter how well we understand the individual parts of a situation, it doesn’t necessarily mean we understand the whole. We see the trees, in other words, but manage to miss the forest. But the sum of most things is greater (or less, I suppose), than its parts, which is why point to a part in retrospect doesn’t make us any better at predicting the future. It’s also perhaps why Watts, who emigrated from the field of physics, virtually always formulaic and rational, understands better than anyone how social science is a trickster god in the pantheon of scientific disciplines; his introduction (which I thought strange at the time) was the story of his defection and the generally poor reception of social science by those who are expecting functional, mechanistic knowledge of the engineer or the chemist.
Personally, I’m just glad I’m not the only one who can’t explain Facebook.
- I’m not technically sure if it’s a subtitle or proper the title.[↩]
- This is a poor example, since at least in Job’s case we have two data points, namely Apple’s position after his first tenure, and well into his second, and the comparison to the interstices.[↩]
- Watts is employed a research arm of Yahoo![↩]