{
  "annotation_id": "0e07d7b4-e120-4517-b2a4-6a7a11efa89b",
  "date_created": "2026-02-17T05:53:07.768000Z",
  "date_modified": "2026-02-17T05:53:07.768000Z",
  "file_hash": "4a73ea6625f39dbffdc8bf8187270841418547ef203a9742214b82481544347e",
  "private": false,
  "record": {
    "abstract": "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\u0027s 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.",
    "arxiv_id": "2601.06557",
    "authors": [
      "Chao Li",
      "Ilia Derevitskii",
      "Sergey Kovalchuk"
    ],
    "categories": [
      "eess.SY",
      "cs.MA",
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels",
    "url": "https://arxiv.org/abs/2601.06557",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "10dd9b82-69b8-45f9-9c6d-edfe95cfe392",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}