delta

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