Suppose I’m a fish farmer, running an experiment with a new type of feed for my fish. In one particular tank, I have 100 fish, and I measure the weight of each fish. I model the weight of each fish as an independent draw from the same distribution, and I want to estimate the mean of that distribution.
Key point: even if I measure every single one of the 100 fish, I will still have some uncertainty in the distribution-mean. Sample-mean is not distribution-mean. My ability to control the fish’ environment is limited; if I try to re-run the experiment with another tank of fish, I don’t think I’ll actually be drawing from the same distribution (more precisely, I don’t think the “same distribution” model will correctly represent my information anymore). Once I measure the weights of each of the 100 fish, that’s it - there’s no more fish I can measure to refine my estimate of the distribution-mean by looking at the physical world, even in principle. Maybe I could gain some more information with detailed simulations and measurements of tank-parameters, but that would be a different model, with a possibly-different distribution-mean.
The distribution-mean is not fully determined by variables in the physical world.
And this isn’t some weird corner-case! This is one of the simplest, most prototypical use-cases of probability/statistics. It’s in intro classes in high-school.
Another example: temperature. We often describe temperature, intuitively, as representing the average kinetic energy of molecules, bouncing around microscopically. But if we look at the math, temperature works exactly like the distribution-mean in the fish example: it doesn’t represent the actual average energy of the particles, it represents the mean energy of some model-distribution from which the actual particle-energies are randomly drawn. Even if we measured the exact energy of every particle in a box, we’d still have nonzero (though extremely small) uncertainty in the temperature.
One thing to note here: we’re talking about purely classical uncertainty. This has nothing to do with quantum mechanics or the uncertainty principle. Quantum adds another source of irresolvable uncertainty, but even in the quantum case we will also have irresolvable classical uncertainty.
Clustering provides another lens on the same phenomenon. Consider this clustering problem:
Let’s say that the lower-left cluster contains trees, the upper-right apples, and the lower-right pencils. I want to estimate the distribution-mean of some parameter for the tree-cluster.
If there’s only a limited number of data points, then this has the same inherent uncertainty as before: sample mean is not distribution mean. But even if there’s an infinite number of data points, there’s still some unresolvable uncertainty: there are points which are boundary-cases between the “tree” cluster and the “apple” cluster, and the distribution-mean depends on how we classify those. There is no physical measurement we can make which will perfectly tell us which things are “trees” or “apples”; this distinction exists only in our model, not in the territory. In turn, the tree-distribution-parameters do not perfectly correspond to any physical things in the territory.
My own work on abstraction implicitly claims that this applies to high-level concepts more generally (indeed, Bayesian clustering is equivalent to abstraction-discovery, under this formulation). It still seems likely that a wide variety of cognitive algorithms would discover and use similar clusters - the clusters themselves are “natural” in some sense. But that does not mean that the physical world fully determines the parameters. It’s just like temperature: it’s a natural abstraction, which I’d expect a wide variety of cognitive processes to use in order to model the world, but that doesn’t mean that the numerical value of a temperature is fully determined by the physical world-state.
Takeaway: the variables in our world-models are not fully determined by features of the physical world, and this is typical, it’s true of most of the variables we use day-to-day.
Our Models Still Work Just Fine
Despite all this, our models still work just fine. Temperature is still coupled to things in the physical world, we can still measure it quite precisely, it still has lots of predictive power, and it’s useful day-to-day. Don't be alarmed; it all adds up to normality.
The variables in our models do not need to correspond to anything in the physical world in order to be predictive and useful. They do need to be coupled to things in the physical world, we need to be able to gain some information about the variables by looking at the physical world and vice versa. But there are variables in our world models which are not fully determined by physical world-state. Even if we knew exactly the state of the entire universe, there would still be uncertainty in some of the variables in our world-models, and that’s fine.