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arXiv:2305.06021 · v4

Orders between channels and implications for partial information decomposition

首次提交 2023-05-10 18:12
版本更新 2026-11-06 22:48
雷达观测 2026-11-09 13:22
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摘要

The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical information theory alone does not provide a unique way to decompose information in this manner and additional assumptions have to be made. Recently, Kolchinsky proposed a new general axiomatic approach to obtain measures of redundant information, based on choosing a preorder relation between information sources (equivalently, a preorder between communication channels). In this paper, we exploit this approach to introduce three new measures of redundant information (and the resulting decompositions) based on well-known preorders between channels, thus contributing to the enrichment of the PID landscape. We relate the new decompositions to existing ones, study some of their properties, and provide examples illustrating their novelty. As a side result, we prove that any preorder that satisfies Kolchinsky's axioms yields a decomposition that meets the axioms originally introduced by Williams and Beer when they first propose the PID.

作者备注:Corrected version of the article published in Entropy 2023, 25, 975 (https://doi.org/10.3390/e25070975). The corrections are described in a correction notice in Entropy. 13 pages, 1 figure, 4 tables

期刊信息:Entropy 2023, 25(7), 975

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v4 · 2026-11-06 22:48 · 最新观测版本

Orders between channels and implications for partial information decomposition

The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical information theory alone does not provide a unique way to decompose information in this manner and additional assumptions have to be made. Recently, Kolchinsky proposed a new general axiomatic approach to obtain measures of redundant information, based on choosing a preorder relation between information sources (equivalently, a preorder between communication channels). In this paper, we exploit this approach to introduce three new measures of redundant information (and the resulting decompositions) based on well-known preorders between channels, thus contributing to the enrichment of the PID landscape. We relate the new decompositions to existing ones, study some of their properties, and provide examples illustrating their novelty. As a side result, we prove that any preorder that satisfies Kolchinsky's axioms yields a decomposition that meets the axioms originally introduced by Williams and Beer when they first propose the PID.

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