Epistemic Generativity: A Structural Definition
Abstract
This paper introduces a formal definition of epistemic generativity as a structural property of evaluative systems. It distinguishes systems that optimise under a fixed evaluative structure from those capable, in principle, of operating on that structure itself. Contemporary artificial intelligence systems are characterised by optimisation relative to externally specified criteria: they select actions or outputs according to given standards but do not revise those standards. Epistemic generativity, by contrast, is defined as the condition under which a system can select among, and revise, its own evaluative criteria in response to evidence. The paper argues that prevailing uses of “generativity” in machine learning and adjacent fields—typically associated with behavioural novelty, compositional capacity, or scale—do not capture this distinction, as they remain consistent with optimisation under fixed evaluation. It further clarifies the status of out-of-distribution failure as performance degradation relative to existing criteria, rather than evidence against those criteria, absent endogenous revision. On this account, behavioural sophistication does not entail evaluative authorship: intelligence, understood as effective optimisation, is structurally distinct from epistemic agency. The analysis provides a minimal, classificatory criterion for separating optimisation from generativity and grounds implications for responsibility and governance. Where evaluative structure remains external to a system’s operation, accountability attaches to those who specify, constrain, and update the standards under which the system functions.
Keywords
- epistemic generativity
- evaluative structure
- epistemic agency
- evaluative authorship
- epistemic enclosure
- domain of operation
- optimization
- out-of-distribution (OOD)
- decision theory
- AI governance
- artificial intelligence