ANALYSIS OF THE INNOVATION OF AI DELIVERY TECHNOLOGY BASED ON EVOLUTIONARY GAME THEORY

Abstract

The widespread application of artificial intelligence technology in logistics delivery has made technological innovation the core driver of industry development. Given that existing evolutionary game studies rarely address collaborative innovation mechanisms in logistics scenarios, this study constructed a tripartite evolutionary game model involving “government-logistics enterprises-R&D institutions.” It delved into the strategic evolution patterns and key influencing factors among these three entities in AI technological innovation for logistics delivery. The findings reveal: (1) The system exhibits multiple evolutionarily stable equilibrium points, with (government incentives, logistics enterprises introduction, R&D institutions technological innovation) representing the ideal equilibrium state. The system demonstrates strong convergence properties, ultimately converging toward the optimal collaborative state. (2) Factors such as innovation costs, introduction costs, and government subsidies significantly influence the evolutionary process. Increased government subsidies to logistics enterprises and R&D institutions accelerate convergence toward the ideal equilibrium, while higher innovation costs and introduction costs slow convergence. (3) The probability of initial strategies adopted by actors affects evolutionary efficiency. Higher initial cooperation willingness among the government, logistics enterprises, and R&D institutions accelerates the system’s convergence to the ideal equilibrium. This study enriches the research perspective on AI-driven logistics delivery technology innovation and provides valuable management insights for government formulation of collaborative innovation policies, as well as cooperation between logistics enterprises and R&D institutions.