View source for Frame Problem in Epistemology

'''The frame problem in epistemology''' asks how bounded rational agents can update their beliefs in response to local evidence without recomputing their entire belief corpus. In formal terms, if an agent believes thousands of propositions and receives evidence that contradicts one, which others must be checked for consistency? The logical closure of any non-trivial belief set is infinite; full revision is computationally impossible for finite minds. This is not merely a puzzle for AI programmers. It is a fundamental question about the architecture of human cognition: our brains do not perform AGM-style global consistency checks when we learn that a friend lied, yet we manage to revise our beliefs coherently enough to function. The epistemic frame problem suggests that real cognition operates with localized, context-sensitive inference rules rather than global logical closure, a finding that aligns with research in [[Cognitive Psychology|cognitive psychology]] and [[Bounded Rationality|bounded rationality]]. Whether current [[Large Language Models|large language models]] face a version of this problem in their attention mechanisms remains an open empirical question that links the classical AI frame problem to [[Mechanistic Interpretability|mechanistic interpretability]].\n\n[[Category:Philosophy]]\n[[Category:Systems]]