# Towards an extended taxonomy of information dynamics via Integrated Information Decomposition

arXiv: https://arxiv.org/abs/2109.13186 · HTML: https://mlsanity.com/paper/2109.13186/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks](https://mlsanity.com/paper/2210.02996/) (2022)
- [A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning](https://mlsanity.com/paper/2601.06851/) (2026)
- [Harmonizing Program Induction with Rate-Distortion Theory](https://mlsanity.com/paper/2405.05294/) (2024)
- [The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness](https://mlsanity.com/paper/2506.13825/) (2025)

## Abstract (arXiv)

Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than what is readily explainable in terms of the `sum of the parts.' Our ability to understand and control these systems remains limited, one reason being that we still don't know how best to describe -- and quantify -- the higher-order dynamical interactions that characterise their complexity. To address this limitation, we combine principles from the theories of Information Decomposition and Integrated Information into what we call Integrated Information Decomposition, or PhiID. PhiID provides a comprehensive framework to reason about, evaluate, and understand the information dynamics of complex multivariate systems. PhiID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer and dynamical complexity as aggregates of these modes. Via computational and empirical examples, we demonstrate that PhiID extends our explanatory power beyond traditional causal discovery methods -- with profound implications for the study of complex systems across disciplines.