数智平台参与农业科技成果转化的 多主体博弈行为研究.

[Objective) The study aimed to provide theoretical underpinnings for optimizing the mechanism of agricultural science and technology transformation by analyzing the multi-agent decision-making process through an evolutionary game model involving three parties: the suppliers of technological innovations, digital intelligence platforms and demanders of technology. (Method) Based on the dynamic evolutionary game theory, a tripartite payoff matrix was constructed in the past, and through numerical simulation analysis utilizing Matlab, the impacts of various parameters such as initial willingness, explicit and implicit costs, and revenue distribution on the decision-making behaviors of the agricultural science and technology achievement transformation entities were analyzed. (Result) (i) Assuming that the returns of all subjects choosing the 'active participation' strategy were all positive, the unstable point evolved along the saddle point E₁ (0,0,0) and ultimately reached the stable point Es (1,1,1); (ii) The intensity of the initial willingness determined the direction of cooperation. As the initial willingness of all three parties increased, the trend towards active cooperation accelerated; (iii) Explicit cost and implicit costs were also important factors determining the direction of cooperation. As either explicit or implicit costs increased, the speed of moving towards the equilibrium point of positive cooperation slowed down; (iv) The higher the allocation ratio of any subject, the greater the probability of choosing 'active participation'. However as the allocation ratio of a certain subject was too high, the probability of other subjects choosing 'active participation' decreased. [Conclusion】 To expedite the pace and efficiency of technology transformation, it is imperative to create an environment that enhances the initial willingness of all parties, reduces collaboration costs, and ensures balanced consideration of each party's interests in the distribution of benefits. Recommendations include constructing a network of digital intelligence platforms for agricultural science and technology transformation, increasing investment in the development and maintenance of digital intelligence platforms, standardizing the agricultural science and technology transformation practices, and refining the mechanisms for benefit distribution among all stakeholders. [ABSTRACT FROM AUTHOR]