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A Hierarchical Framework With Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive

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    This study introduces a new framework for detecting emergence in complex adaptive systems (CASs). The hierarchical approach accurately identifies emergent behaviors by learning spatial and temporal patterns from local agent observations.

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    Area of Science:

    • Complex Adaptive Systems (CASs)
    • Network Science
    • Artificial Intelligence

    Background:

    • Emergence is a key property of complex adaptive systems (CASs), observable in real-world systems like traffic networks.
    • Detecting emergence aids in system monitoring and early warnings for detrimental phenomena.
    • Current methods struggle with capturing spatial patterns and nonlinear agent interactions in decentralized systems.

    Purpose of the Study:

    • To propose a novel hierarchical framework for detecting emergence in CASs using local agent observations.
    • To address limitations of existing methods in capturing spatial patterns and nonlinear relationships.
    • To develop a method for accurate emergence detection in decentralized systems.

    Main Methods:

    • A hierarchical framework with spatio-temporal consistency learning (HSTCL) was developed.
    • Spatio-temporal encoders (STEs) utilizing spatial and temporal transformers were employed.
    • Self-supervised learning minimized spatial and temporal dissimilarities in latent space for consistent representations.

    Main Results:

    • The HSTCL method demonstrated superior accuracy in detecting emergent behaviors compared to traditional and deep learning approaches.
    • The framework successfully captured nonlinear agent relationships and complex system evolution.
    • Performance was validated on three datasets featuring challenging emergent phenomena.

    Conclusions:

    • The proposed HSTCL framework offers a robust and accurate solution for emergence detection in CASs.
    • The hierarchical and generic nature allows integration with other deep learning techniques.
    • This approach enhances system monitoring and the management of complex dynamic systems.