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

• URL: http://arxiv.org/abs/2401.10300v2
• Date: Mon, 28 Oct 2024 03:33:04 GMT
• Title: A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems
• Authors: Siyuan Chen, Xin Du, Jiahai Wang,
• Abstract summary: Emergence, a global property of complex adaptive systems, is prevalent in real-world dynamic systems, e.g., network-level traffic congestions. This paper proposes a hierarchical framework with CAS-temporal consistency to solve these two problems by learning the system representation and agent representations. Our method achieves more detection than traditional methods and deep learning methods on three datasets with well-known yet hard-to-detect emergent behaviors.
• Score: 41.055298739292695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emergence, a global property of complex adaptive systems (CASs) constituted by interactive agents, is prevalent in real-world dynamic systems, e.g., network-level traffic congestions. Detecting its formation and evaporation helps to monitor the state of a system, allowing to issue a warning signal for harmful emergent phenomena. Since there is no centralized controller of CAS, detecting emergence based on each agent's local observation is desirable but challenging. Existing works are unable to capture emergence-related spatial patterns, and fail to model the nonlinear relationships among agents. This paper proposes a hierarchical framework with spatio-temporal consistency learning to solve these two problems by learning the system representation and agent representations, respectively. Spatio-temporal encoders composed of spatial and temporal transformers are designed to capture agents' nonlinear relationships and the system's complex evolution. Agents' and the system's representations are learned to preserve the spatio-temporal consistency by minimizing the spatial and temporal dissimilarities in a self-supervised manner in the latent space. Our method achieves more accurate detection than traditional methods and deep learning methods on three datasets with well-known yet hard-to-detect emergent behaviors. Notably, our hierarchical framework is generic in incorporating other deep learning methods for agent-level and system-level detection.

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