Incentive Mechanisms for Information Collaboration in Agri-Food Supply Chains: An Evolutionary Game and System Dynamics Approach - Rui Meng; Decheng Fan; Xinliang Xu (Systems)
Information collaboration is a core driver of digital transformation and efficiency improvement in agri-food supply chains. This study constructs a quadripartite evolutionary game model involving the government, an information service platform, farmers, and agri-food enterprises. By integrating system dynamics, it analyzes stakeholders’ strategic interactions and evolutionary pathways while exploring the regulatory effects of key parameters in reward and penalty mechanisms on system convergence. The key findings are as follows: (1) The system reaches a stable equilibrium regardless of initial strategy combinations. (2) The reward–penalty mechanism is essential for equilibrium stability, but the reward amount and allocation ratios must meet threshold constraints. (3) Given the significant path-dependent lock-in effect in agri-food enterprises’ strategy convergence under static parameters, a dynamic parameter configuration scheme is proposed to reshape convergence and optimize equilibrium. The simulation results indicate that dynamic parameter regulation sacrifices the regulatory efficiency of the information service platform to enhance the overall collaboration. A joint dynamic reward–penalty strategy improves efficiency but delays platform convergence, whereas a single dynamic incentive offers a balanced trade-off. Based on this, an incentive framework is developed to guide government incentive design. This study expands the theoretical framework of information collaboration in AFSCs and provides practical guidance for policymakers.