Schilling, Andreas (2025) Randomness, Quantum Uncertainty, and Emergence: A Suggestion for Testing the Seemingly Untestable. [Preprint]

	Text Randomness, Quantum Uncertainty, and Emergence.pdf Download (231kB)
	Text Randomness, Quantum Uncertainty, and Emergence.pdf Download (231kB)
	Text Randomness, Quantum Uncertainty, and Emergence.pdf Download (232kB)

Abstract

The functioning of complex natural structures, such as living systems, has eluded a generally accepted theoretical basis and empirical verification for decades, partly because of a lack of meaningful experiments. We therefore propose a class of experiments designed to test whether hitherto unknown principles of order are at work in sufficiently complex natural dynamical systems that cannot be captured by known physical laws. Part of the underlying working hypothesis is that the quantum mechanical uncertainty principle leaves room for ordering phenomena in chaotic or nearly chaotic physical systems in the sense of a strong emergence principle, which would not be expected when treated according to conventional modelling approaches, as has already been formulated by several authors in various forms. Unlike some previous proposals which included coherent quantum-mechanical states, we do not require any macroscopic quantum coherence. The key idea behind testing this undoubtedly bold hypothesis is to compare two sufficiently complex, virtually identical setups, one of which is operating with deterministic pseudo-random number generators placed at certain key points that are sensitive to small changes, while the other is equipped with quantum-based physical random-number generators, the two setups being otherwise identical. Existing artificial neural networks are proposed as suitable test objects for this purpose, and the overall performance under identical training conditions could be used as a quantitative benchmark. As the working hypothesis used goes far beyond artificial networks, a successful outcome of such an experiment could have strong implications in many other branches of science.

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Item Type:	Preprint
Creators:	Creators Email ORCID Schilling, Andreas andreas.schilling@physik.uzh.ch 0000-0002-3898-2498
Keywords:	Complex systems, neural networks, emergence, uncertainty principle, randomness
Subjects:	Specific Sciences > Artificial Intelligence General Issues > Computer Simulation General Issues > Determinism/Indeterminism Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Theory/Observation
Depositing User:	Prof. Andreas Schilling
Date Deposited:	25 May 2025 13:17
Last Modified:	25 May 2025 13:17
Item ID:	25437
Subjects:	Specific Sciences > Artificial Intelligence General Issues > Computer Simulation General Issues > Determinism/Indeterminism Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Theory/Observation
Date:	13 May 2025
URI:	https://philsci-archive.pitt.edu/id/eprint/25437

Available Versions of this Item

• Randomness, Quantum Uncertainty, and Emergence: A Suggestion for Testing the Seemingly Untestable. (deposited 15 May 2025 12:18)
  • Randomness, Quantum Uncertainty, and Emergence: A Suggestion for Testing the Seemingly Untestable. (deposited 19 May 2025 13:04)
    • Randomness, Quantum Uncertainty, and Emergence: A Suggestion for Testing the Seemingly Untestable. (deposited 25 May 2025 13:17) [Currently Displayed]

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