Database Reference
In-Depth Information
Gutierrez:
What's an interesting project that you've worked on?
Wiggins:
One example comes from 2001 when I was talking to a mathema-
tician whom I respect very much about what he saw as the future of our field,
the intersection of statistics and biology, and he said, “Networks. It's all going
to be networks.” I said, “What are you talking about? Dynamical systems
on networks?” He said, “Sure, that and statistics of networks. Everything on
networks.”
At the time, the phrase “statistics of networks” didn't even parse for me. I
couldn't even understand what he was saying. He was right. I saw him again at a
conference on networks two years later.
5
Many people that I really respected
spoke at that conference about their theories of the way real-world networks
came to evolve.
I remember stepping off the street corner one day while talking to another
biophysicist, somebody who was coming from the same intellectual tradition
that I had with my PhD. And I was saying, “People look at real-world networks,
and they plot this one statistical attribute, and then they make up different
models—all of which can reproduce this one statistical attribute.” And they're
basically just looking at a handful of predefined statistics and saying, 'Well, I can
reproduce that statistical behavior.' That attribute is over-universal. There are
too many theories and therefore too many theorists saying that they could
make models that looked like real-world graphs. You know what we should
do? We should totally flip this problem on its head and build a machine learn-
ing algorithm that, presented with a new network, can tell which of a few
competing theorists wins. And if that works, then we're allowed to look at
a real-world network and see which theorist has the best model for some
network that they're all claiming to describe.”
That notion of an algorithm for model testing led to a series of papers that
I think were genuinely orthogonal to what anybody else was doing. And I
think it was a good example of seeing people whom I respect and think are
very smart people but who were not using the right tool for the right job,
and then trying to reframe a question being asked by a community of smart
people as a prediction problem. The great thing about predictions is that you
can be wrong, which I think is hugely important. I can't sleep at night if I'm
involved in a scientific field where you can't be wrong. And that's the great
thing about predictions: It could turn out that you can build a predictive model
that actually is just complete crap at making predictions, and you've learned
something.
