Tom Griffiths

So we have that abstract computational level, what should you be doing?

And then there's the algorithmic level, which is what's a good strategy for trying to get close to that?

And you can ask a question at that level, which is about,

you know, what's the best that you could do at trying to do the thing you're supposed to be doing with particular constraints on the resources that are available to you?

And then when we characterize what those constraints are, we can actually work out what that looks like.

And in many cases, that actually gives us insight into some of the, you know, what might seem like strange things that people do.

And that's something that I've done a bunch of work on.

And we actually have a book coming out in a few weeks, which is about that idea of, we call it resource rationality.

And the book explores

how to change our notions of rationality as a consequence of recognizing those constraints.

It's very awkward.

They're coming out within a week of one another.

One is more for a general audience and exploring these ideas about the laws of thought, and then the other is a slightly more academic book which is focused on this idea of what we should do with our limited cognitive resources.

I think the way that I would think about it is that one of the interesting

paradoxes of human cognition is that we are both, from the perspective of a psychologist, error-prone decision makers who use these heuristics to result in biases, and from the perspective of computer scientists, these aspirational agents that are doing the kinds of things that we'd like our AI systems to do.

And so if you want to resolve that paradox, the way that I resolve it is to say, we are

good at solving the kinds of problems that we face with the resources that we have, right?

And you can think about that as being the consequences of different kinds of adaptation.

So evolution as well as learning, right?

So over the course of our lifetime, learning to use our cognitive resources better, right?