The Perennial Problem

Rafe:
So, I wanted to touch base on that idea of combinatorial explosion because it’s very important in understanding the perennial problem and how we have failed to address that now. We tend to think about problems as things that maybe have a very specific solution, or we’re really excited about … We talk about algorithms. Right? Algorithms are everywhere right now.

Craig:
Right.

Rafe:
Theoretically, an algorithm is anything that allows you to derive a perfect solution to a problem. Right? But there’s actually two classes of problems. One is what you could call a well defined problem, which is one where you can search the entire problem space. You can look at every possible solution and find one that is correct. And then there’s ill defined problems that have problem spaces that are too large.

Rafe:
So chess, you have 30 potential moves on average per turn that are legal, and you have 60 turns on average per game. Which means that the pathways that are available in a chess game are 30 to the 60, which is comparable to the number of atoms in the universe. That’s not a search space that you can functionally search. It would take infinity for you to figure out all of the possible permutations of chess.

Rafe:
So, in those, we have to operate with heuristics. Right? So, a heuristic could be like control the center of the board. Right? Get your queen out early. Castle your king. Those are heuristics. Okay, so the problem with heuristics is that the same thing as a heuristics is a bias.

Craig:
Right.

Rafe:
Right? Stereotypes are heuristics. As movement teachers, we face the problem that motor control is completely heuristic. There’s no algorithmic solution to any physical problem that a human has because a human has a combinatorial explosive set of physical capacities.

Craig:
Yeah. In one of your episodes maybe like four months ago, you were talking about how people traditionally thought of the human body as a machine.

Rafe:
Exactly.

Craig:
And that you can mechanistically combine these systems to produce perfect movement.

Rafe:
Yep.

Craig:
And when one says that, it’s pretty clear. It’s like, yeah, humans are not machines. And that seems to fit into this on the physiological side of this problem. That’s why more holistic training methods produce better results, because you’re not trying to teach them, “This is exactly how you do this. This is how you move in these environments.” So, if that’s the case, if we’re missing those tools in the more psychological aspect of it, I don’t know whether to ask what have we deleted or what tools are we missing or maybe what tools should we add in [crosstalk 00:24:42].

Rafe:
Yeah. Yeah, I want to expand on that and just lay out Vervaeke’s argument. But while we’re on the subject of the body as a machine, I think it’s a really powerful example of how we fall into these heuristic biases and how they can mislead us.

Rafe:
We are inherently symbolic thinkers. Right? We think within metaphors. If you start drilling down into language, the vast majority of the things that we say are in some way metaphorical. So, when we say that we understand something, we’re standing under it. Right? Do you have an optimal grip on that, right? Do you catch what I’m saying?

Craig:
Right. Right.

Rafe:
All of those are physical metaphors. Right? So, we live within metaphors. And one thing that happens is we tend to utilize the metaphors of the most advanced technology that we have at one time. So, when we were really getting good at making clocks, we started to imagine the workings of the universe as a clock. Right? And this was the origin of a determinist approach to metaphysics.