Finding emergence in data by maximizing effective information
This work addresses the problem of identifying emergent phenomena in complex dynamical systems for researchers in fields like neuroscience, offering an incremental framework based on causal emergence theory.
The paper tackles the challenge of quantifying emergence and modeling emergent dynamics in complex systems by introducing a machine learning framework that maximizes effective information to learn macro-dynamics and quantify causal emergence, demonstrating effectiveness on simulated and real data with improved generalization across test environments.
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.