Rethinking Computation in Unstable Environments: The Heuristic Machine Proposal

This paper introduces the Heuristic Machine as a new model of computation designed to operate under symbolic instability, semantic drift, and epistemic collapse. Rather than executing predefined instructions or navigating probabilistic states, the Heuristic Machine compresses degraded meaning to sustain cognitive coherence where axiomatic systems fail. Grounded in the framework of Heuristic Physics, it shifts the objective of computation from solving within formal rules to surviving the breakdown of those rules themselves. By redefining core computational functions through four adaptive axes — Compression, Collapse, Cognition, and Creation — this architecture proposes a mechanism for meaning persistence in environments where logical formalism is insufficient. Positioned alongside traditional models such as the Turing Machine and the Quantum Computer, the Heuristic Machine extends the computational landscape into domains characterized by contradiction, ambiguity, and symbolic entropy. Rather than seeking precision, it preserves interpretability. Rather than halting, it adapts. This proposal aims to initiate a new epistemic pathway for building systems that remain intelligible in the face of conceptual degradation.