Finding emergence in data by maximizing effective information

TL;DR

This paper presents a machine learning framework that quantifies and models emergent phenomena in complex systems by maximizing effective information, enabling better understanding and prediction of macro-level dynamics from data.

Contribution

It introduces a novel framework inspired by causal emergence theory that learns macro-dynamics in an emergent latent space and quantifies the degree of causal emergence from data.

Findings

Effectively quantifies degrees of causal emergence across various conditions.

Learns a one-dimensional macro-state from fMRI data representing neural activities.

Shows improved generalization to different test environments.

Abstract

Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level. Thus, it's crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data.