Tom Griffiths

As well as just sort of engaging in some planning or we call it meta reasoning about how to appropriately approach different kinds of problems that we're trying to solve.

That's a good question.

I haven't thought about it in those terms.

The way that he sets that up is more in terms of a kind of objective that the system has rather than in terms of a resource constraint.

And the way that we think about it is a little more explicitly saying, if we want to redefine what rationality is in a way that works for agents with finite computational resources, this is drawing on an idea from the AI literature from Stuart Russell and Eric Horvitz,

The way to define what rationality is for a bounded agent is more in terms of

Instead of focusing on taking the action that's the action that probability theory and so on tells you you should take, it's using the best algorithm to choose the action that you're going to take.

It's popping up a level of abstraction in terms of thinking about defining rationality at that meta level as a tool for then generating what are the appropriate ways of using your cognitive resources at the

what we call the object level, the actions you take in the world.

And my previous book, Algorithms to Live By, is actually a pretty good general audience treatment of those ideas.

We didn't express it in terms of this framework of resource rationality, but it's really about the idea that in some ways computer science provides a better guide to rationality than

you know, economics, right?

Yeah, so some of the kinds of strategies we use are the kinds of things people have identified as heuristics.

Heuristic just means a rule of thumb or a shortcut for solving a problem.

I think part of what is valuable about reanalyzing those from the perspective of resource rationality is being able to say,

that using those heuristics isn't necessarily a bad thing.

The biases that come from those might not necessarily be things that you can avoid given the cognitive resources that you're operating with.

Instead, we can ask a question like, are we doing the best job we could with the cognitive resources that we have?

Then is there a way that we could mitigate those biases by maybe using a different heuristic in a particular setting or something like that?

And so the kinds of strategies that we focus on in our work on resource rationality are things like sampling strategies for approximating Bayesian inference.