Cognitive Information Theory
Structural Limits of Objective-Function AI through Irreducible Entropy, Causal Ambiguity, and Sequential Distribution Drift
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Abstract
Contemporary AI assumes accuracy improves monotonically with scale. We challenge this through Cognitive Information Theory (CIT), which decomposes cognitive information into a reducible component (structural complexity) and two irreducible ones (channel entropy and causal ambiguity). For systems governed by a scalar objective (the class Sof ), CIT proves that when either irreducible component is non-zero, no amount of data, capacity, or calibration guarantees error-free predictions.
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