This paper presents a PDE-based auto-aggregation model for simulating descriptive norm dynamics in autonomous multi-agent systems, capturing convergence and violation through non-local perception kernels and external potential fields. Extending classical transport equations, the framework represents opinion popularity as a continuous distribution, enabling direct interactions without Bayesian guessing of beliefs. Applied to a real-world COVID-19 dataset from a major medical center, the experimental results demonstrate that: when clinical guidelines serve as a top-down constraint mechanism, it effectively generates convergence of novel descriptive norms consistent with the dataset; in the bottom-up experiment, potential field guidance successfully promotes the system's reconstruction of descriptive norms aligned with the dataset through violation-and-recoupling; whereas fully autonomous interaction leads to the emergence of multi-centric normative structures independent of the dataset.

翻译：本文提出了一种基于偏微分方程的自聚集模型，用于模拟自主多智能体系统中的描述性规范动态，通过非局部感知核与外部势场捕捉规范的趋同与违反行为。该框架拓展了经典的输运方程，将意见流行度表示为连续分布，使得智能体能够直接交互而无需对信念进行贝叶斯猜测。应用于来自某大型医疗中心的真实世界COVID-19数据集，实验结果表明：当临床指南作为自上而下的约束机制时，该模型能有效生成与数据集一致的新型描述性规范的趋同；在自下而上的实验中，势场引导通过违反-再耦合过程成功促进了系统重建与数据集相符的描述性规范；而完全自主的交互则会导致独立于数据集的多中心规范结构的涌现。