That’s a good take: treating trust as “some kind of structured uncertainty object over futures” is very close to what I was gesturing toward because a bare scalar clearly isn’t sufficient.
On reflection, I have to admit I was using “trust” a bit loosely in the post. What it’s become clear to me I’m really trying to model isn’t trust in the common usage sense (intentions, warmth, etc.), but something structural: roughly, how stable someone’s behavior is under visible strain, and who tends to bear the cost when things get hard. In my head it’s closer to a relational stability/reliability profile than trust per se, but trust had been the mental shorthand I was employing.
That’s also why I’d be a bit cautious about equating this model of trust with “how much I can constrain my uncertainty about them doing things I wouldn’t want.” Predictability and trust can come apart: I can have very low uncertainty that someone will reliably screw me over, but that doesn’t make them high-trust. I think that interpretation is actually right for the actual content of what I was describing in the post and the mismatch comes from my loose language (so thanks for this comment because it was the impetus to make a change I’d had kicking around for a minute.)
It seems like we need both a representation of a distribution over future behaviors/trajectories, and a way to mark which regions of that space are good for me/the system vs “bad”.
What’s most important to me is modeling without needing to pretend to know someone’s internals. The visibility/strain/cost/memory breakdown is my attempt at that: who shows up where, what pressures they’re under, who actually eats the cost, and how that pattern evolves over time.
All that said, I really like the intuition of “not a scalar but a distribution-like object.” In my head, what’s coming together is something like a trajectory-based stability profile built from a few real-valued measurable signals rather than a full-blown complex wavefunction. I’ve got another post in the works that goes into more detail and once that’s formalized soon I’m certainly open to revisiting the modeling to see where these concepts intersect.
That’s a good take: treating trust as “some kind of structured uncertainty object over futures” is very close to what I was gesturing toward because a bare scalar clearly isn’t sufficient.
On reflection, I have to admit I was using “trust” a bit loosely in the post. What it’s become clear to me I’m really trying to model isn’t trust in the common usage sense (intentions, warmth, etc.), but something structural: roughly, how stable someone’s behavior is under visible strain, and who tends to bear the cost when things get hard. In my head it’s closer to a relational stability/reliability profile than trust per se, but trust had been the mental shorthand I was employing.
That’s also why I’d be a bit cautious about equating this model of trust with “how much I can constrain my uncertainty about them doing things I wouldn’t want.” Predictability and trust can come apart: I can have very low uncertainty that someone will reliably screw me over, but that doesn’t make them high-trust. I think that interpretation is actually right for the actual content of what I was describing in the post and the mismatch comes from my loose language (so thanks for this comment because it was the impetus to make a change I’d had kicking around for a minute.)
It seems like we need both a representation of a distribution over future behaviors/trajectories, and a way to mark which regions of that space are good for me/the system vs “bad”.
What’s most important to me is modeling without needing to pretend to know someone’s internals. The visibility/strain/cost/memory breakdown is my attempt at that: who shows up where, what pressures they’re under, who actually eats the cost, and how that pattern evolves over time.
All that said, I really like the intuition of “not a scalar but a distribution-like object.” In my head, what’s coming together is something like a trajectory-based stability profile built from a few real-valued measurable signals rather than a full-blown complex wavefunction. I’ve got another post in the works that goes into more detail and once that’s formalized soon I’m certainly open to revisiting the modeling to see where these concepts intersect.