6533b861fe1ef96bd12c466e
RESEARCH PRODUCT
Gradients of O-information: Low-order descriptors of high-order dependencies
T. ScagliariniD. NuzziY. AntonacciL. FaesF. E. RosasD. MarinazzoS. Stramagliasubject
FOS: Computer and information sciencesPhysics and AstronomyInformation Theory (cs.IT)Computer Science - Information TheoryPhysics - Data Analysis Statistics and ProbabilitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaFOS: Physical sciencesGeneral Physics and Astronomycomplex systems information theory dynamical systems econophysicsData Analysis Statistics and Probability (physics.data-an)description
O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterise how high-order effects are localised across a system of interest. We illustrate the capabilities of the proposed framework by revealing the role of specific spins in Ising models with frustration, and on practical data analysis on US macroeconomic data. Our theoretical and empirical analyses demonstrate the potential of these gradients to highlight the contribution of variables in forming high-order informational circuits
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2023-01-01 | Physical Review Research |