Symbolic local information transfer

TL;DR

This paper extends permutation-information theoretic methods to analyze local information transfer in continuous-state spatiotemporal systems, introducing symbolic local transfer entropies and demonstrating their effectiveness on model systems.

Contribution

It introduces symbolic local transfer entropy measures that adapt permutation-based methods for continuous systems, enabling local information transfer analysis.

Findings

Effective in characterizing local information transfer in continuous systems

Successfully applied to coupled map lattice and Bak-Sneppen models

Reveals spatiotemporal profiles of distributed computations

Abstract

Recently, the permutation-information theoretic approach has been used in a broad range of research fields. In particular, in the study of highdimensional dynamical systems, it has been shown that this approach can be effective in characterizing global properties, including the complexity of their spatiotemporal dynamics. Here, we show that this approach can also be applied to reveal local spatiotemporal profiles of distributed computations existing at each spatiotemporal point in the system. J. T. Lizier et al. have recently introduced the concept of local information dynamics, which consists of information storage, transfer, and modification. This concept has been intensively studied with regard to cellular automata, and has provided quantitative evidence of several characteristic behaviors observed in the system. In this paper, by focusing on the local information transfer, we demonstrate that the application of the permutation-information theoretic approach, which introduces natural symbolization methods, makes the concept easily extendible to systems that have continuous states. We propose measures called symbolic local transfer entropies, and apply these measures to two test models, the coupled map lattice (CML) system and the Bak-Sneppen model (BS-model), to show their relevance to spatiotemporal systems that have continuous states.