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Born rule and many-worlds interpretation of quantum mechanics: an ergodic approach towards the ontological aspects of the interpretation.

  • Published In: Pramana: Journal of Physics, 2026, v. 100, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Varshovi, Amir Abbass 3 of 3

Abstract

In this paper, the ontological aspects of the many-worlds interpretation (MWI) of quantum mechanics are studied by employing measure-theoretical methods of the ergodic theory. Before everything it is shown that the frequency interpretation of the Born rule is independent of the measurement postulate and hence, must be extended to the statistical ontology of the MWI. Then, based on the Birkhoff's ergodic theorem we will prove that except for a Lebesgue null set of extremely rare parallel worlds, the Born rule will be refuted in all the parallel worlds created in a long-term process of consecutive experiments in the MWI of quantum mechanics. This reduces the unlimited ontology of the MWI to an extremely confined but still uncountable set of possible parallel worlds in long-term processes and allow us to acknowledge a pseudo-deterministic mechanism of quantum mechanics in the statistical picture of the theory. Hence, we conclude that the common understanding of the unconstrained MWI ontology, even if considered valid, cannot survive in long-term consecutive observations and must soon collapse into a more constrained interpretation of quantum mechanics. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Pramana: Journal of Physics. 2026/03, Vol. 100, Issue 1, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2026
  • ISSN:0304-4289
  • DOI:10.1007/s12043-025-03054-8
  • Accession Number:191498065
  • Copyright Statement:Copyright of Pramana: Journal of Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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