English

Characterising high-order interdependence via entropic conjugation

Information Theory 2024-10-15 v1 math.IT Data Analysis, Statistics and Probability

Abstract

High-order phenomena play crucial roles in many systems of interest, but their analysis is often highly nontrivial. There is a rich literature providing a number of alternative information-theoretic quantities capturing high-order phenomena, but their interpretation and relationship with each other is not well understood. The lack of principles unifying these quantities obscures the choice of tools for enabling specific type of analyses. Here we show how an entropic conjugation provides a theoretically grounded principle to investigate the space of possible high-order quantities, clarifying the nature of the existent metrics while revealing gaps in the literature. This leads to identify novel notions of symmetry and skew-symmetry as key properties for guaranteeing a balanced account of high-order interdependencies and enabling broadly applicable analyses across physical systems.

Keywords

Cite

@article{arxiv.2410.10485,
  title  = {Characterising high-order interdependence via entropic conjugation},
  author = {Fernando E. Rosas and Aaron Gutknecht and Pedro A. M. Mediano and Michael Gastpar},
  journal= {arXiv preprint arXiv:2410.10485},
  year   = {2024}
}

Comments

8 pages, 1 figure

R2 v1 2026-06-28T19:20:34.745Z