# Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game
## 4 Results and Discussion
### Stable Strategy Simulation  
In order to more intuitively reflect the game behavior selection of the business organization node, common user node and regulator node under the situation of data element sharing, we used Matlab(R2023a) software to conduct a numerical simulation analysis of the evolution and stability strategy of the tripartite game subjects under the change in different parameters and verify the above theoretical analysis. Due to the complexity of the convergence media ecology, the research model in this article cannot be fully validated by substitution in the convergence media ecology for the time being, which is also a shortcoming of the research content in this article. In order to verify the effectiveness of the model as accurately as possible, the model parameters in the simulation and verification process of this article are randomly generated according to the requirements to meet the general principles of the experiment. The numerical simulation foundation is set as follows: simulation time span is 0 to 5, business organization node \"positively participate in data sharing\" strategy probability from 0.1 to 0.9, an interval of 0.2; common user node takes the probability of the \"data sharing\" strategy from 0.1 to 0.9, an interval of 0.2; regulator node takes the probability of \"regulation\" strategy from 0.1 to 0.9, an interval of 0.2. In the tripartite evolution stability strategy simulation diagram, the blue curve represents the probability curve of the business organization node choosing to positively participate in data element sharing, the green curve represents the probability curve of the common user node choosing to participate in data element sharing, and the red curve represents the probability curve of the regulator node choosing to participate in data element sharing regulation. In this paper, we extracted four special equilibrium points, E1(0,0,0), E2 (0,0,1), E7 (1,1,0) and E8 (1,1,1), for sampling simulation analysis. The specific values of parameter simulation are shown in Table 4.  
(1) Numerical simulation analysis of the equilibrium point E1(0,0,0)  
When the parameter assignment of group 1 is used, the stability condition of equilibrium point E1(0,0,0) is met. Based on this condition, numerical simulation was conducted, and the initial values of x, y and z were randomly specified. The tripartite evolutionary game stable strategy diagram is shown in Figure 8. The initial values of x, y and z are set as 0.1, 0.1 and 0.1, and the evolution is carried out according to the step size of 0.2. A tripartite evolutionary game three-dimensional simulation diagram is shown in Figure 9.  
On the basis of the first set of parameter data, the values of \(G_{11}\) were set, respectively, as 25, 50 and 75, and the initial participation probabilities of the tripartite evolutionary game (\(x\),\(y\),\(z\)) were randomly set, and the simulation results of replicating the dynamic equations with \(G_{11}\) changes are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**ID** & \(\mathbf{E_{11}}\) & \(\mathbf{E_{12}}\) & \(\mathbf{G_{11}}\) & \(\mathbf{G_{12}}\) & \(\mathbf{I_{1}}\) & \(\mathbf{F_{1}}\) & \(\mathbf{E_{2}}\) & \(\mathbf{G_{2}}\) & \(\mathbf{I_{2}}\) & **R** & \(\mathbf{G_{3}}\) & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\mathbf{F_{3}}\) & **H** \\ \hline
1 & 15 & 10 & 25 & 7 & 10 & 5 & 10 & 25 & 5 & 5 & 20 & 0.3 & 0.5 & 5 & 5 \\ \hline
2 & 15 & 10 & 25 & 7 & 10 & 5 & 10 & 20 & 15 & 5 & 20 & 0.3 & 0.5 & 25 & 10 \\ \hline
3 & 15 & 10 & 25 & 7 & 10 & 5 & 10 & 30 & 10 & 5 & 20 & 0.3 & 0.5 & 25 & 5 \\ \hline
4 & 15 & 10 & 25 & 7 & 10 & 5 & 10 & 25 & 20 & 5 & 20 & 0.3 & 0.5 & 10 & 5 \\ \hline \end{tabular}
\end{table} 
Table 4: Numerical simulation value. Figure 8: Tripartite evolutionary game stable strategy diagram of E1(0,0,0).