Stochastic evolutionary game analysis of tacit knowledge sharing in patent commercialization

Given the rapid prevalence and advancement of patent commercialization, tacit knowledge sharing during this process is increasingly being scrutinized. To explore the factors affecting tacit knowledge sharing in patent commercialization, this paper constructed a tripartite stochastic evolutionary game model to analyze the complex game interaction between inventors, enterprises, and patent intermediaries. Due to uncertainty in the external environment, Gaussian white noise was introduced into replicator dynamic equations, and numerical simulation was used to describe the tripartite dynamic evolution. The results showed the following: (1) inventors and enterprises with similar levels of relevant capabilities are more likely to achieve better results in sharing tacit knowledge. (2) The implementation of an effective market reputation evaluation system has the potential to enhance implicit knowledge sharing in the realm of patent commercialization. (3) The percentage of payments of the two-stage payment contract will affect the behavior of inventors and enterprises. (4) The stability of the equilibrium solution is closely tied to the intensity of Gaussian white noise, which can lead to deviations from the original trajectory of equilibrium strategies. Greater external uncertainty makes tacit knowledge sharing more challenging. This study will provide constructive suggestions for promoting tacit knowledge sharing among participants in patent commercialization.