Modeling Complex Systems with Weighted Multi-agent Hypergraph

The study and analysis of complex systems are confronted with significant challenges due to their inherent adaptability and uncertainty. Traditional modeling techniques focused on individual components of the system are insufficient for capturing the whole character of the system, as complex system structures constantly evolve, leading to non-repetitive outcomes. The unreliability of traditional modeling methods is exacerbated by the unpredictability of complex systems. The limitations of graph-based modeling approaches lie in their reduced capacity to analyze the complex interdependencies among nodes, as well as the low-order and high-order associations that exist between them. The hypergraph weight method, by focusing on the relationships between nodes, captures the unique complex connections of complex systems and provides support for modeling. Therefore, this paper proposes a new approach to model complex systems using hypergraph weights to facilitate multi-agent decision-making. This method enhances the accuracy of analyzing high-order relationships between nodes and improves the precision of complex results. The proposed technique presents innovative ideas and strategies for the intelligent theoretical investigation of complex systems.