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Emergence, a global property of complex adaptive systems (CASs) constitutedby interactive agents, is prevalent in real-world dynamic systems, e.g.,network-level traffic congestions. Detecting its formation and evaporationhelps to monitor the state of a system, allowing to issue a warning signal forharmful emergent phenomena. Since there is no centralized controller of CAS,detecting emergence based on each agent's local observation is desirable butchallenging. Existing works are unable to capture emergence-related spatialpatterns, and fail to model the nonlinear relationships among agents. Thispaper proposes a hierarchical framework with spatio-temporal consistencylearning to solve these two problems by learning the system representation andagent representations, respectively. Especially, spatio-temporal encoders aretailored to capture agents' nonlinear relationships and the system's complexevolution. Representations of the agents and the system are learned bypreserving the intrinsic spatio-temporal consistency in a self-supervisedmanner. Our method achieves more accurate detection than traditional methodsand deep learning methods on three datasets with well-known yet hard-to-detectemergent behaviors. Notably, our hierarchical framework is generic, which canemploy other deep learning methods for agent-level and system-level detection.

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