Dilemma and sustainable development strategy of sharing teaching resources for big data majors in universities based on game theory - Chunzhong Li; Chenglan Liu; Wenliang Ju (Discover Artificial Intelligence)
Abstract In the process of sharing teaching resources for big data majors in universities, the competition and cooperation between universities are complex, and there are problems such as unfair resource allocation and information asymmetry. Resource sharing faces many difficulties. In response to these issues, this article constructed a dynamic information evolution game model based on game theory, which integrated three sub models: static game, incomplete information game, and evolutionary game. The static game model was used to analyze the strategic choices of universities in a certain environment; the incomplete information game model revealed the game behavior under information asymmetry; the evolutionary game model simulated the dynamic evolution process of university strategies. This article combined a simulation case of the teaching resource sharing project for big data majors in university alliances, and used this model to identify and analyze the practical difficulties of resource sharing in universities, revealing the deep-seated reasons why resource sharing is difficult to achieve in competitive and cooperative relationships. On this basis, this article proposed a series of sustainable development strategies, including optimizing resource allocation mechanisms, improving information transparency, etc., providing theoretical support and practical reference for the effective sharing of teaching resources in big data majors in universities. Experimental data showed that in the resource sharing simulation process of five universities (A, B, C, D, E), the strategy adjustment values of each university decreased from around 0.8 to below 0.2.