239 citations · 898 across the 33 of their papers we have counts for
36 papers
Null models for comparing information decomposition across complex systems
Alberto Liardi, Fernando E. Rosas, Robin L. Carhart-Harris +3
A key feature of information theory is its universality, as it can be applied to study a broad variety of complex systems. However, many information-theoretic measures can vary sig…
Characterising high-order interdependence via entropic conjugation
Fernando E. Rosas, Aaron Gutknecht, Pedro A. M. Mediano +1
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 inf…
The Fast Möbius Transform: An algebraic approach to information decomposition
Abel Jansma, Pedro A. M. Mediano, Fernando E. Rosas
The partial information decomposition (PID) and its extension integrated information decomposition (ID) are promising frameworks to investigate information phenomena involving m…
Characterising directed and undirected metrics of high-order interdependence
Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar
Systems of interest for theoretical or experimental work often exhibit high-order interactions, corresponding to statistical interdependencies in groups of variables that cannot be…
Software in the natural world: A computational approach to hierarchical emergence
Fernando E. Rosas, Bernhard C. Geiger, Andrea I Luppi +4
Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent adv…
Quantifying Hierarchical Selection
Hardik Rajpal, Clem von Stengel, Pedro A. M. Mediano +4
At what level does selective pressure effectively act? When considering the reproductive dynamics of interacting and mutating agents, it has long been debated whether selection is…
Information decomposition reveals hidden high-order contributions to temporal irreversibility
Andrea I Luppi, Fernando E. Rosas, Gustavo Deco +2
Temporal irreversibility, often referred to as the arrow of time, is a fundamental concept in statistical mechanics. Markers of irreversibility also provide a powerful characterisa…
Quantifying synergy and redundancy in multiplex networks
Andrea I. Luppi, Eckehard Olbrich, Conor Finn +4
Understanding how different networks relate to each other is key for obtaining a greater insight into complex systems. Here, we introduce an intuitive yet powerful framework to cha…
Dynamical noise can enhance high-order statistical structure in complex systems
Patricio Orio, Pedro A. M. Mediano, Fernando E. Rosas
Recent research has provided a wealth of evidence highlighting the pivotal role of high-order interdependencies in supporting the information-processing capabilities of distributed…
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics
Fernando E. Rosas, Diego Candia-Rivera, Andrea I Luppi +2
Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of p…
Thermodynamics of exponential Kolmogorov-Nagumo averages
Pablo A. Morales, Jan Korbel, Fernando E. Rosas
This paper investigates generalized thermodynamic relationships in physical systems where relevant macroscopic variables are determined by the exponential Kolmogorov-Nagumo average…
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
Alexandra M. Proca, Fernando E. Rosas, Andrea I. Luppi +3
Striking progress has recently been made in understanding human cognition by analyzing how its neuronal underpinnings are engaged in different modes of information processing. Spec…
Gradients of O-information: low-order descriptors of high-order dependencies
Tomas Scagliarini, Davide Nuzzi, Yuri Antonacci +4
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 com…
An information-theoretic approach to hypergraph psychometrics
Daniele Marinazzo, Jan Van Roozendaal, Fernando E. Rosas +5
Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evalua…
Disentangling high-order mechanisms and high-order behaviours in complex systems
Fernando E. Rosas, Pedro A. M. Mediano, Andrea I. Luppi +5
Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements,…
Geometric Structures Induced by Deformations of the Legendre Transform
Pablo A. Morales, Jan Korbel, Fernando E. Rosas
The recent link discovered between generalized Legendre transforms and non-dually flat statistical manifolds suggests a fundamental reason behind the ubiquity of Rényi's divergence…
Greater than the parts: A review of the information decomposition approach to causal emergence
Pedro A. M. Mediano, Fernando E. Rosas, Andrea I. Luppi +5
Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons…
Towards an extended taxonomy of information dynamics via Integrated Information Decomposition
Pedro A. M. Mediano, Fernando E. Rosas, Andrea I Luppi +4
Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than…
Quantifying high-order interdependencies on individual patterns via the local O-information: theory and applications to music analysis
Tomas Scagliarini, Daniele Marinazzo, Yike Guo +2
High-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer,…
Integrated information as a common signature of dynamical and information-processing complexity
Pedro A. M. Mediano, Fernando E. Rosas, Juan Carlos Farah +3
The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanatio…
A generalization of the maximum entropy principle for curved statistical manifolds
Pablo A. Morales, Fernando E. Rosas
The maximum entropy principle (MEP) is one of the most prominent methods to investigate and model complex systems. Despite its popularity, the standard form of the MEP can only gen…
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles
Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar
Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these a…
Hyperharmonic analysis for the study of high-order information-theoretic signals
Anibal M. Medina-Mardones, Fernando E. Rosas, Sebastián E. Rodríguez +1
Network representations often cannot fully account for the structural richness of complex systems spanning multiple levels of organisation. Recently proposed high-order information…
Causal blankets: Theory and algorithmic framework
Fernando E. Rosas, Pedro A. M. Mediano, Martin Biehl +2
We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics. Our approach is based on the notio…