Stephen Wolfram

And that one number is kind of the growth rate of the arms or some such other thing.

That fails to capture sort of the detail of what's going on inside the system.

And that's, in a sense, a big challenge for science is how do you extract from the natural world, for example, those aspects of it,

that you are interested in talking about.

Now, you might just say, I don't really care about the fluffiness of the snowflakes.

All I care about is the growth rate of the arms, in which case you can have a good model without knowing anything about the fluffiness.

But the fact is, as a practical, if you say, what is the most obvious feature of a snowflake?

Oh, that it has this complicated shape.

Well, then you've got a different story about what you model.

I mean, this is one of the features of sort of modeling in science that, you know, what is a model?

A model is some way of reducing the actuality of the world to something where you can readily sort of give a narrative for what's happening, where you can basically make some kind of abstraction of what's happening and answer questions that you care about answering.

If you wanted to answer all possible questions about the system, you'd have to have the whole system because you might care about this particular molecule.

Where did it go?

And your model, which is some big abstraction of that, has nothing to say about that.

So one of the things that's often confusing in science is people will have, I've got a model, somebody says.

Somebody else will say, I don't believe in your model because it doesn't capture the feature of the system that I care about.

There's always this controversy about, is it a correct model?

Well, no model, except for the actual system itself, is a correct model in the sense that it captures everything.

The question is, does it capture what you care about capturing?

Sometimes that's ultimately defined by what you're going to build technology out of, things like this.