This invention provides a method,
system, device, and medium for optimizing catastrophe reinsurance contracts, belonging to the insurance field. The method includes: constructing a multi-agent
system; employing a mathematical optimization model, combined with core clauses in the reinsurance contract, to design a dynamically optimized reinsurance contract mechanism to optimize the contract terms; designing a
collaboration and game mechanism among the agents, and using multi-agent
reinforcement learning to dynamically optimize the
game strategy to adjust the contract terms in real time. A strategy combining game theory and
reinforcement learning is used to optimize the contract terms between the insurance company and the reinsurance company. The game mechanism helps the agents find the optimal balance between competition and cooperation, while
reinforcement learning enables the agents to continuously adjust their strategies in a dynamic market. This ensures that the reinsurance contract terms can be automatically optimized in response to market changes, disaster events, and changes in regulatory requirements. The agents optimize the contract terms through a real-time feedback mechanism, ensuring a balance between
risk sharing and profitability.