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.

翻译：涌现作为由交互主体构成的复杂自适应系统（CAS）的整体属性，普遍存在于现实动态系统中（例如网络级交通拥堵）。检测其形成与消散过程有助于监控系统状态，从而对有害涌现现象发出预警信号。由于CAS缺乏集中控制器，基于每个主体的局部观测检测涌现虽具理想性却充满挑战。现有方法无法捕捉与涌现相关的空间模式，亦未能建模主体间的非线性关系。本文提出一种融合时空一致性学习的层次化框架，通过分别学习系统表征和主体表征来解决上述两个问题。具体而言，定制化时空编码器用于捕捉主体间非线性关系与系统复杂演化过程。通过自监督方式保持内在时空一致性来学习主体与系统的表征。在三个包含已知但难检测涌现行为的数据集上，本方法相较传统方法与深度学习方法实现了更精准的检测。值得注意的是，该层次化框架具有通用性，可结合其他深度学习方法进行主体级与系统级检测。