Work

A Symmetric Bayesian Framework for Swarm Information Interaction and Collective Behavior Prediction

Year: 2025

Type: article

Abstract: This paper studies the information interaction process in Bayesian theorem-based swarm systems. Through theoretical analysis, model construction, and simulation experiments, it explores how Bayesian decision-making utilizes information cascades to update its state step by step in group information interaction. The system operates within a theoretical framework where an underlying symmetry governs the dynamic combination of prior knowledge, neighbor information, and target guidance, leading to spontaneous aggregation behavior similar to biological swarms. A key embodiment of this symmetry is the action–reaction force parity between agents, which ensures local stability.The simulation results show that groups with different prior information exhibit a multi-stage convergence characteristic, which reveals that within each iteration step, the agent adheres to the rules for information-symmetric communication and interaction. This dynamic behavior is a true reflection of natural biological populations and provides theoretical support for practical applications such as traffic management and robot collaboration.

Source: Symmetry

DOI: 10.3390/sym17122091

Authors: Hui Shen, Peng Yu, Yonghui Yang, Chenyang Li, Xue-Bo Chen

Institutions: University of Science and Technology Liaoning, Wuxi Taihu University

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China