When Noise Becomes Information: The Possible Limits of Algorithmic Explanation
Year: 2026
Type: preprint
Abstract: This article questions whether the predictive success of algorithmic descriptions warrants treating them as complete accounts of physical organisation. It distinguishes stability from organisation and argues that genuinely random fluctuations cannot, by themselves, explain the persistence of information-bearing structure whose coherence depends on information not already contained in the governing dynamics. Fluctuations treated as noise at one descriptive level may contain correlations or structure that become causally relevant at another. This possibility does not challenge established physical laws, but it questions the completeness of algorithmic explanation: predictive adequacy within a model should not be equated with the ontological completeness of the description. This framework is exemplified at the molecular scale, where Levinthal’s paradox and interfacial water dynamics suggest that protein folding relies on phase-correlated environmental fluctuations rather than isotropic stochastic search.
Source: Preprints.org
Author: Pavel Straňák
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204211920