  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. Especially, spatio-temporal encoders are
tailored to capture agents' nonlinear relationships and the system's complex
evolution. Representations of the agents and the system are learned by
preserving the intrinsic spatio-temporal consistency in a self-supervised
manner. 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, which can
employ other deep learning methods for agent-level and system-level detection.
