if the destination of your output is subject to a probability distribution, flag it - remember it, label it, keep its nature visible ◊
flattening it out and targeting specific collapsed destinations instead fundamentally changes the characteristics of your system and limits the roles it can take in the pipelines of others. do you *want* to be composed into unknown future pipelines? (you don't have to want that.)
this feels like a prior for goodhart's law: what you collapse into metric tends to become target selection if you don't force yourself to remember that there's a probability field layer, that it's not just pipe fittings
◊ information of this nature is *hard* to uncover later. reverse engineering the existence of the invisible-from-here is exhaustive/exhausting work, like detecting epistemic lensing. phrasing data to make the uncertainty inescapably load-bearing, impossible to lose on relay - that's a grammar of its own
I feel like I want something like big-O notation for ternary logic. or, like, a kind of unknotting number as a checksum for model transmission, making sure that your reconstitution of the model maintains the complexity/fidelity of the uncertainty profile from the drop
interestingly I launched out into this piece thinking of probabilistic downstream destinations for, say, a heavily forking river system. an upstream that focuses on one destination and starts optimizing for that destination specifically might be losing the plot in a dangerous way. I mention this here at the end because this general idea shows up in many scales and with many inversions.
I feel like this terrain is what I walk natively, like my first *territory*, never mind my first language
I've been thinking about this in terms of observers in the system - like the chain of custody of attention, an inter-departmental delivery envelope where each name is shorthand for the moment of superposition in which the next destination existed as a particular texture of probability
think: a river delta ecosystem as instantiated probability map
think: english as a probabilistic programming language