## 4 Experimental Setup and Results
### Ablation Experiment Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Experimental Group** & \multicolumn{3}{c}{**Control Group**} \\ \cline{2-5}  & **M** & **SD** & **M** & **SD** \\ \hline Before & 3.97 & 0.23 & 3.93 & 0.22 \\ After & 3.90 & 0.31 & 3.37 & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: PE group scores before and after experiment. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Category (Mean \(\pm\) SD)** & **t** & \(\mathbf{p}\) \\ \hline Experimental Group (n = 10) & 3.97 \(\pm\) 0.23 & & \\ Control Group (n = 10) & 3.93 \(\pm\) 0.22 & 0.55 & 0.588 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of \(t\)-test for NE group before experiment. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Category (Mean \(\pm\) SD)** & **t** & \(\mathbf{p}\) \\ \hline Experimental Group (n = 10) & 3.90 \(\pm\) 0.23 & & \\ Control Group (n = 10) & 3.93 \(\pm\) 0.22 & 0.55 & 0.588 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Results of \(t\)-test for NE group before experiment. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Category (Mean \(\pm\) SD)** & **t** & \(\mathbf{p}\) \\ \hline Experimental Group (n = 10) & 3.90 \(\pm\) 0.31 & & \\ Control Group (n = 10) & 3.37 \(\pm\) 0.26 & 4.36 & 0.000 *** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Results of \(t\)-test for NE group after experiment. that have overlooked the influence of immediate emotions and provides new insights for designing efficient human-machine hybrid decision systems. However, despite these achievements, our study has several limitations. For instance, the diversity of our sample was limited, which restricts the generalizability of our results. Future research could address this limitation by incorporating a broader and more diverse sample, which would enhance the external validity of the findings. Moreover, although we discussed the technical challenges related to authority allocation and system explainability, these remain unresolved. In future studies, exploring new algorithms and models, such as ensemble learning and deep learning approaches, could enhance the transparency and usability of the system. In addition, although Kuhn poker provides us with a simplified experimental environment, its application may not fully represent the more complex real-world decision-making scenarios, such as those in medical artificial intelligence, financial risk analysis, or autonomous driving. In these practical applications, the decision-making process involves not only multivariable analysis but also a high degree of uncertainty, making the scenarios much more complex than those in the poker model. Specifically, in the field of medical AI, real-world medical decisions face additional complexities, including the emotional factors of patients, uncertainty in medical knowledge, and interactions between various stakeholders. Future research could explore how the game strategies from Kuhn poker can be applied to medical decision systems, particularly in handling incomplete information and dynamic situations, and how to effectively balance the interests of all parties. In the field of financial risk analysis, participants in the financial market often face high uncertainty and rapidly changing risk factors. Game theory models similar to Kuhn poker can be used to analyze investor behavior, thereby developing more effective risk management strategies. Future studies should focus on how these game strategies can be integrated with the ever-changing environment of financial markets to improve the accuracy of risk analysis and decision-making. For autonomous driving systems, the real-time decision-making process involves interactions between multiple traffic agents, and coordinating the risk preferences and cooperation willingness of different agents is crucial. Future research could further extend the model from this study, considering more complex traffic scenarios and dynamic decision-making processes, exploring how to effectively coordinate decisions among multiple intelligent agents.