Alison Gopnik: prediction

Transcript context

…I think of the kids as much more Bayesian than the scientists, and here’s what worries me about the scientists. When they revise their views, the direction in which they move is almost always predictable. If they’re, say, thinking over decades, how much does the money supply matter? They’ll move a bit in the direction of thinking it matters. Then they’ll move a bit more. Then finally, they might decide, well, it matters quite a bit. It shouldn’t be predictable if they’re Bayesian. It should be more like a random walk. Basically, they’re stubborn. Then what I observe, if they’re proven to be wrong, they don’t usually admit it, whereas a child might. They just start working on something else. That, too, is a funny way of dealing with being wrong, right? There’s a beautiful idea that I’ve been using thinking about the kids that is in computer science. It actually comes from physics called simulated annealing. The idea behind simulated annealing is that you have some problem to solve. You have some space of solutions that you’re trying to get to. One thing you can do, which is like what you’re describing about the money supply, is just make little changes to what you already know. That’s what you mean about moving in the predictable direction. You’re just changing things a little bit. Then seeing, “Okay, if I change it a little bit, is it doing a better job of accounting for the data?” That’s what people think of as a low-temperature search. The other kind of search you can do, the high-temperature search, is just bounce around the space. Try wild, crazy things. Exactly as you were saying, have just a more random walk. The strategy that you see in computer science, this annealing, is start out with this wild, crazy, out-of-the-box, high-temperature search through the space, and then cool off and just fill in the details. If you think about your four-year-old, who do they sound like? Do they sound like the creature that’s just moving a little bit, or do they sound like they’re noisy and bouncy and random and doing all sorts of weird things? The four-year-old seemed to be a really good idea of this kind of random search. But if you’re a scientist, of course, you have to balance those things. You can’t just think of crazy new ideas. You have to figure out how you’re going to test them. You have to get grant proposals. In science, you’re always going back and forth between, “Do I do the high-temperature, wild, crazy, out-of-the-box search? Do I think of ideas that don’t look like they would be very likely to begin with? Or do I fill in the details?” I think you see both things happening. When you get big paradigm shifts, as Kuhn said, when you get big changes in science, a lot of times it’s because someone found an idea that looked like it was improbable. The nice thing about kids is, because they don’t have to worry about grant proposals, they can be off in the wild space all the time. Here’s a few things a four-year-old might do. I’m going to ask you which one is the most Bayesian. A four-year-old pulls his sister’s hair, a four-year-old tries to figure out how to use a fork properly, and a four-year-old tries to put together the pieces of a puzzle. Or pick your own nomination. In which do we see the child being the most Bayesian and then the least Bayesian?…

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