Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information
Information Theory
2019-09-18 v1 math.IT
Neurons and Cognition
Abstract
This article introduces a model-agnostic approach to study statistical synergy, a form of emergence in which patterns at large scales are not traceable from lower scales. Our framework leverages various multivariate extensions of Shannon's mutual information, and introduces the O-information as a metric capable of characterising synergy- and redundancy-dominated systems. We develop key analytical properties of the O-information, and study how it relates to other metrics of high-order interactions from the statistical mechanics and neuroscience literature. Finally, as a proof of concept, we use the proposed framework to explore the relevance of statistical synergy in Baroque music scores.
Cite
@article{arxiv.1902.11239,
title = {Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information},
author = {Fernando Rosas and Pedro A. M. Mediano and Michael Gastpar and Henrik J. Jensen},
journal= {arXiv preprint arXiv:1902.11239},
year = {2019}
}