Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels

TL;DR

This paper introduces a PDE-based auto-aggregation model for simulating how descriptive norms evolve in multi-agent systems, capturing convergence, violations, and norm formation through adaptive perception kernels and external potentials.

Contribution

It extends classical transport equations to model opinion distributions and demonstrates real-world applicability with COVID-19 data, showing how top-down and bottom-up influences shape norm dynamics.

Findings

Top-down constraints lead to norm convergence.

Potential fields help reconstruct norms aligning with data.

Autonomous interactions produce multi-centric normative structures.

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'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.