Generalized Measures of Information Transfer

TL;DR

This paper extends transfer entropy to differentiate between state-dependent and state-independent transfer, as well as among unique, redundant, and synergistic information transfer from multiple sources.

Contribution

It introduces novel measures that distinguish types of information transfer, enhancing analysis of complex systems beyond traditional transfer entropy.

Findings

New measures differentiate state-dependent and state-independent transfer.

The measures identify unique, redundant, and synergistic transfer in systems.

Demonstrations on systems extend previous literature examples.

Abstract

Transfer entropy provides a general tool for analyzing the magnitudes and directions---but not the \emph{kinds}---of information transfer in a system. We extend transfer entropy in two complementary ways. First, we distinguish state-dependent from state-independent transfer, based on whether a source's influence depends on the state of the target. Second, for multiple sources, we distinguish between unique, redundant, and synergistic transfer. The new measures are demonstrated on several systems that extend examples from previous literature.