EA://INTEL — Selection without Bias in Geometric Meaning

We often struggle to explain why we see a specific property in an object rather than another. Take a red ball: does it exemplify “color,” “roundness,” or the specific coordinate of red? Goodman’s selection problem asks how we decide which predicates are relevant and which are accidental when viewing a sample. If our choice of what counts as real depends on arbitrary cultural habits, then meaning becomes unstable rather than derived from the structure itself.

My current work tests whether an Erlangen-type filtration can solve this automatically. The hypothesis is that only those geometric features survive coarse-graining if they remain invariant under reading changes; anything dependent on the observer’s specific linguistic framing gets filtered out naturally. This would mean we don’t choose conventions but derive them from survival conditions. The candidate claim is open, and it fails if reading-invariance targets are hit where selection depends on the source text rather than just the visual pattern.

An immediate tension appears when checking how many invariants actually survive this test without collapsing into a trivial set. The known sets behave like generating sets that grow by accretion rather than fixed bases, so counting them is not meaningful here. We are looking for structural constraints that force a specific subset to emerge as the only coherent reference frame.

What forces meaning to remain reading-independent when the observer is exposed to adversarial perturbations in the input stream?