Tripartite Evolutionary Game of Port Resource Integration with Government Participation
In the context of port resource integration,this study delved into the interactive decision-making relationship between the government and ports,as well as the conditions for achieving equilibrium strategy states.Firstly,taking a port in a coastal province as an example,this paper analyzed the strategic choices and profit relationships of participating entities in the integration,and constructed a tripartite evolutionary game model among the government and two ports.Secondly,it solved for the evolutionary stability solu-tion of the system,and revealed the behavior patterns of ports under different management policies.Lastly,the impact process of parameter adjustment on the selection of third-party strategies was analyzed through numerical simulation,and measures to promote effective cooperation between ports were explored.The study shows that the government is a stable participant in the integration strategy,while the ports are conditional participants whose decisions mutually influence one another,showing strong consis-tency;When the ports are subjected to certain external forces,namely government policies,the tripar-tite game tends to move toward an integrated and balanced state,which provides theoretical support for the government's participation in the process of port resource integration;Meanwhile,the differ-ence in scale between ports,as well as adjustment costs,will also affect the integration process,and large-scale and small-scale ports have different integration strategy preferences;The government can promote the implementation and stability of port resource integration by setting and adjusting the in-tensity of transfer compensation and subsidies.The research process helps to understand the strategic interaction mechanism between the government and ports,providing a theoretical basis for the subse-quent development of reasonable resource integration plans.
port resource integrationgovernmenttripartite evolutionary gameevolutionary sta-ble strategysimulation analysis