Multi-Agent Collaborative Modeling for Systemic Risk Propagation in Financial Markets: A Game-Theoretic Framework

This paper focuses on the risk control challenges arising from behavioral interactions among multiple participants in financial markets. It proposes a financial risk game framework based on multi-agent collaborative modeling. The framework integrates environment state encoding, strategy generation networks, and a coordinated evolution mechanism to dynamically model the propagation paths of systemic risk in complex markets. During the modeling process, each agent generates behavior strategies independently based on local observations. The market state is then updated over time through a system evolution function, which captures the coupling between multi-agent behaviors and the risk structure. To verify the stability and adaptability of the proposed method, a series of sensitivity experiments are designed. These experiments examine the impact of hyperparameters, data characteristics, and environmental disturbances on system performance. The study focuses on several key factors, including the number of agents, time window length, sampling frequency, and anomaly injection. The experimental results show that the method performs well across multiple dimensions such as strategy stability, modeling consistency, and coordination efficiency. The model demonstrates strong structural representation and dynamic adaptability. Through comparative experiments and disturbance analysis, the study further reveals the model’s capability to simulate the evolution of financial risk structures under various conditions. This provides a valuable methodological reference for intelligent modeling of complex financial systems.