Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915720376352768 |
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| author | Li, Chao Derevitskii, Ilia Kovalchuk, Sergey |
| author_facet | Li, Chao Derevitskii, Ilia Kovalchuk, Sergey |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_06557 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels Li, Chao Derevitskii, Ilia Kovalchuk, Sergey Systems and Control Multiagent Systems 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. |
| title | Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels |
| topic | Systems and Control Multiagent Systems |
| url | https://arxiv.org/abs/2601.06557 |