Novel Inconsistency Results for Partial Information Decomposition
Philip Hendrik Matthias, Abdullah Makkeh, Michael Wibral, Aaron J. Gutknecht

TL;DR
This paper establishes fundamental incompatibility results in Partial Information Decomposition, showing that key properties of classical information theory cannot all hold simultaneously in PID frameworks, guiding future theoretical development.
Contribution
It introduces novel inconsistency theorems using a mereological approach, revealing unavoidable trade-offs among core properties in PID.
Findings
Non-negativity, chain rule, and invariance cannot all hold in PID
Strengthens previous incompatibility results involving classical axioms
Highlights fundamental choices in designing PID frameworks
Abstract
Partial Information Decomposition (PID) seeks to disentangle how information about a target variable is distributed across multiple sources, separating redundant, unique, and synergistic contributions. Despite extensive theoretical development and applications across diverse fields, the search for a unique, universally accepted solution remains elusive, with numerous competing proposals offering different decompositions. A promising but underutilized strategy for making progress is to establish inconsistency results, proofs that certain combinations of intuitively appealing axioms cannot be simultaneously satisfied. Such results clarify the landscape of possibilities and force us to recognize where fundamental choices must be made. In this work, we leverage the recently developed mereological approach to PID to establish novel inconsistency results with far-reaching implications. Our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBayesian Modeling and Causal Inference · Logic, Reasoning, and Knowledge · Statistical Mechanics and Entropy
