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Finding emergence in data by maximizing effective information.

Mingzhe Yang1, Zhipeng Wang1, Kaiwei Liu1

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Summary
This summary is machine-generated.

This study introduces a machine learning framework to model complex system dynamics by identifying emergent phenomena. The approach quantifies causal emergence (CE) and enhances causal effects in macro-dynamics models.

Keywords:
causal emergencecoarse grainingdynamics learningeffective informationinvertible neural network

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Area of Science:

  • Complex Systems Science
  • Machine Learning
  • Dynamical Systems Theory

Background:

  • Modeling emergent behaviors in complex systems is difficult as they are not directly observable from micro-level data.
  • Existing methods struggle to capture macro-level dynamics from available observational data.
  • Causal emergence (CE) theory provides a framework for understanding macro-level causality.

Purpose of the Study:

  • To develop a data-driven machine learning framework for identifying emergent phenomena.
  • To quantify the degree of causal emergence (CE) in complex dynamical systems.
  • To learn macro-dynamics in an emergent latent space.

Main Methods:

  • Developed a machine learning framework inspired by causal emergence (CE) theory.
  • The framework learns macro-dynamics by maximizing effective information in an emergent latent space.
  • Applied the framework to simulated and real-world functional magnetic resonance imaging (fMRI) data.

Main Results:

  • The framework effectively quantifies degrees of causal emergence (CE) under various conditions.
  • Distinct influences of different noise types on CE quantification were revealed.
  • A one-dimensional macro-state representing neural activity was learned from fMRI data.
  • Improved generalization across different test environments was observed in simulation data.

Conclusions:

  • The proposed machine learning framework successfully models macro-dynamics and quantifies causal emergence (CE).
  • The approach offers a robust method for analyzing complex systems and their emergent properties.
  • Demonstrated effectiveness on both simulated data and real-world neuroimaging data.