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This paper reframes contextuality not as a unique quirk of quantum mechanics, but as a fundamental property inherent to probability theory and logic itself. By decoupling this concept from specific physical theories, the work provides a rigorous mathematical foundation that could inform the development of more robust probabilistic models in AI systems. Such theoretical clarity is essential for advancing machine learning architectures that must operate under constraints of incomplete or context-dependent data.
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On Contextuality as a Feature of Logic and Probability Theory

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arXiv:2603.11857v2 Announce Type: replace Abstract: In quantum mechanics, not everything that can be observed can be observed simultaneously. Observational data exhibits \emph{contextuality} -- a generalisation of nonlocality -- if the result of an observation is necessarily dependent on which combination of observables was measured. This article gives a mathematical introduction to contextuality, emphasising its nature as a general feature of probability theory and logic, rather than of any particular quantum theory.

Abstract:In quantum mechanics, not everything that can be observed can be observed simultaneously. Observational data exhibits \emph{contextuality} -- a generalisation of nonlocality -- if the result of an observation is necessarily dependent on which combination of observables was measured. This article gives a mathematical introduction to contextuality, emphasising its nature as a general feature of probability theory and logic, rather than of any particular quantum theory.

Quantum Physics (quant-ph); Logic (math.LO); Probability (math.PR)

arXiv:2603.11857 [quant-ph]

(or arXiv:2603.11857v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2603.11857 Focus to learn more arXiv-issued DOI via DataCite

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