For the sake of argument, let’s be real generous with the terms “unambiguous”, “sequence”, “goal”, and “recognizable” and say everything is an algorithm if you squint hard enough.
when you soften these words, what you’re left with is a heuristic - a method that occasionally does what you expect but that’s underspecified. it’s a decision procedure where the steps aren’t totally clear or that sometimes arrives at unexpected results because it fails to capture the underlying model of reality at play.
Thanks for the help, but I think I’m still having some trouble understanding what that all means exactly. Could you elaborate on an example where thinking of something as an algorithm results in a clearly and demonstrably worse understanding of it?
Okay, I think I get it now. I see how one could really twist something like your evolution example every which way to make it look like an algorithm. Things like saying the process to crabs is prescribed by the environmental conditions selecting for crab like traits or whatever, but I can see how doing that is so overly broad as to be a useless way to analyze the situation.
One more thing: I don’t know enough about algorithms to really say, but isn’t it possible for an algorithm to produce wildly varying results from nearly identical inputs? Like how a double pendulum is analytically unpredictable. What’s more, could the algorithmic nature of a system be entirely obscured as a result of it being composed of many associated algorithms linked input to output in a net, some of which may even be recursively linked? That looks to me like it could be a source of randomness and ambiguity in an algorithmic system that would be borderline impossible to sus out.
