Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games

At present, utilizing the insurance community is a common method to deal with investment risks along the Belt and Road; however, there is no clear method or mechanism to deal with the decision-making optimization and cost allocation of the insurance community participants. We propose a hybrid approach to solve this problem. First, we construct an underwriting decision optimization model for the insurance community using the multi-choice goal programming method, which generates the cost characteristic function based on a cooperative alliance. Second, we use the cooperative game method combined with the modified Shapley value method to take risk factors into consideration, which allows us to optimize the cost allocation among members of the insurance community. Finally, our simulation analysis results show that the multi-choice goal programming method can optimize the insurance community’s underwriting decisions. Specifically, the total underwriting cost is lower than the sum of the underwriting costs under the insurance company’s single-action strategy, and the total underwriting scale is as large as possible. Compared with the classical Shapley value method, the modified Shapley value method can better reflect differences in the underwriting risks of different regions, encouraging governments to take measures to reduce underwriting risks. To conclude, we propose some suggestions based on our research findings. The possible contributions of this paper are as follows: our research provides a hybrid optimization method based on multiple-choice objective programming and cooperative games to solve the cost allocation problem facing the insurance community, and it has some reference value for improving the cost-sharing system of the insurance community.