City Research Online

Abstract

Scientific inquiry seeks causal explanations of observed phenomena. The Bell experiment provides a paradigmatic case, revealing correlations between spatially separated systems that no local model can reproduce. Such correlations, known as Bell non-locality, are typically analyzed under the non-signalling assumption, which requires that local statistics be independent of distant measurement choices. Yet real experiments, as well as applications beyond physics, often involve signalling, raising the question of how non-locality should be characterized without this constraint. We introduce a general method for quantifying Bell non-locality in the presence of signalling, designed to relax locality as little as necessary. Our approach is guided by the question: how often can locality be preserved across repeated trials in explaining the observed correlations? The task reduces to finding the optimal convex decomposition of the observed correlations into local and genuinely non-local components. We solve this problem within the linear-programming framework, obtaining a closed-form solution valid for arbitrary correlations. We further evaluate a corresponding measure of signalling, demonstrating the generality of the method and the non-trivial character of the results. By extending the analysis of non-locality outside the non-signalling regime, our framework broadens the scope of experimental analysis both within and beyond physics, offering tools for settings where signalling and non-local contributions are difficult to disentangle.

Publication Type:	Article
Additional Information:	© 2026, The Author(s). Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QC Physics
Departments:	School of Health & Medical Sciences School of Health & Medical Sciences > Department of Psychology & Neuroscience
SWORD Depositor:	Symplectic Administrator

Export

Downloads