The Observer-Relative Knowability Hypothesis

Abstract

The concept of unknowability is commonly discussed relative to the cognitive limits of human observers. This paper asks whether a boundary established for one Observer Intelligence can be generalized to heterogeneous observers without an additional assumption of perceptual or epistemic equivalence. It first proposes observer-relative knowability, K = K(O), and then extends the model by introducing the epistemic capability level L, yielding K = K(O, L). Unknowability is subsequently derived as the complement of observer-relative knowability, U(O, L) = S ∖ K(O, L). The resulting hypothesis is that inaccessibility to one observer at one capability level does not by itself establish universal unknowability. The paper illustrates the model with a minimal cross- sectional example, defines its scope relative to interpretation, and considers an implication for artificial intelligence: a human-readable AI interface does not establish epistemic equivalence between Human and AI Observer Intelligences. The model does not claim that AI has access to structures unknowable to humans, nor that multiple observers collectively exhaust Structure. It proposes only that epistemic boundaries should remain indexed to the observer and its capability level unless observer equivalence is independently established. Let S denote an individual Structure, S_all the domain of candidate Structures, O an Observer Intelligence, K its epistemically accessible domain, and L its epistemic capability level. Unknowability U is not introduced as a primary variable; it is derived later from K.