Integrated information as a common signature of dynamical and information-processing complexity

TL;DR

This paper proposes using Integrated Information Theory as a unifying framework to identify common signatures of complexity across dynamical and information-processing systems, demonstrating its effectiveness in diverse models.

Contribution

It introduces a pragmatic application of IIT and $\

Findings

Integrated information captures signatures of metastability and criticality.

It reveals distributed computation and emergent particles in cellular automata.

The approach bridges informational and dynamical complexity perspectives.

Abstract

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behaviour in both dynamical and information-processing systems. Here we argue that a pragmatic use of Integrated Information Theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. Furthermore, by leveraging metrics put forward by the integrated information decomposition ($\Phi$ID) framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity -- including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata -- without relying on idiosyncratic, ad-hoc criteria. These results show how an agnostic use of IIT can provide important steps towards bridging the gap between informational and dynamical approaches to complex systems.