Optimizing Sustainable Agricultural Development via Evolutionary and Stackelberg Games

The study explores the relatively underexamined role of artificial intelligence policies in sustainable agricultural development by investigating how governments, enterprises, and farmers interact under different policy incentives. A combination of tripartite evolutionary and Stackelberg game models is employed to examine how artificial intelligence can support more effective policy design, improve the speed of response, and foster greater collaboration among stakeholders. The analysis primarily draws on simulated data, reflecting the impact of policy incentives across various contexts. Findings suggest that artificial intelligence policies can meaningfully enhance cooperation, thereby promoting sustainable agricultural development. Higher levels of government incentives appear to encourage participation from both enterprises and farmers, while artificial intelligence contributes to faster and more precise policy adjustments. Theoretically, the study offers a framework for understanding artificial intelligence policy in agriculture and elucidates the mechanisms governing stakeholder interactions. From a practical perspective, the results provide cautious guidance for the design of artificial intelligence policies aimed at fostering sustainability.