# Broadcast Channel Cooperative Gain: An Operational Interpretation of Partial Information Decomposition

arXiv: https://arxiv.org/abs/2502.10878 · HTML: https://mlsanity.com/paper/2502.10878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework](https://mlsanity.com/paper/2302.12247/) (2023)
- [Factorized Contrastive Learning: Going Beyond Multi-view Redundancy](https://mlsanity.com/paper/2306.05268/) (2023)
- [A Novel Approach to the Partial Information Decomposition](https://mlsanity.com/paper/1908.08642/) (2019)
- [Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data](https://mlsanity.com/paper/2307.10515/) (2023)
- [Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems](https://mlsanity.com/paper/1411.2832/) (2014)
- [Measuring multivariate redundant information with pointwise common change in surprisal](https://mlsanity.com/paper/1602.05063/) (2016)
- [Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions](https://mlsanity.com/paper/1510.00831/) (2015)
- [A Measure of the Complexity of Neural Representations based on Partial Information Decomposition](https://mlsanity.com/paper/2209.10438/) (2022)
- [Unique Information via Dependency Constraints](https://mlsanity.com/paper/1709.06653/) (2017)

## Built on by (most cited first, 2 of 2)

- [Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions](https://mlsanity.com/paper/2510.04417/) (2025)
- [MLLMs Hallucinate when Information Distribution Drifts in Synergy Heads](https://mlsanity.com/paper/2609.09206/) (2026)

## Abstract (arXiv)

Partial information decomposition has recently found applications in biological signal processing and machine learning. Despite its impacts, the decomposition was introduced through an informal and heuristic route, and its exact operational meaning is unclear. In this work, we fill this gap by connecting partial information decomposition to the capacity of the broadcast channel, which has been well-studied in the information theory literature. We show that the synergistic information in the decomposition can be rigorously interpreted as the cooperative gain, or a lower bound of this gain, on the corresponding broadcast channel. This interpretation can help practitioners to better explain and expand the applications of the partial information decomposition technique.