## 4 Experimental Setup and Results
### Ablation Experiment Results
In conclusion, future research should consider how the findings from Kuhn poker can be extended to these more complex decision-making scenarios to enhance the practical significance and broader applicability of the research, thereby promoting the application of human-machine hybrid decision systems in a wider range of fields. Furthermore, this study employs the CFR algorithm to solve the Kuhn poker approximate Nash equilibrium and uses it as the decision-making basis for the decision support system. However, as the complexity of the experimental scenarios increases, the computational complexity of the CFR algorithm will become a potential limiting factor. Specifically, the size of the tree grows exponentially as the problem scale increases, and the time complexity of the CFR algorithm is \(O(T\cdot|I|\cdot|A|)\), which means that for large-scale instances, the computation time may exceed the acceptable range for practical applications. In this case, alternative algorithms may provide more effective solutions. Heuristic methods (such as genetic algorithms, simulated annealing, or local search methods) can significantly improve computational speed, although they typically sacrifice some degree of optimality. These methods are particularly useful for large-scale instances where response time is crucial. Furthermore, CFR has some scalable variants, such as CFR+, which improves the efficiency of the algorithm by optimizing the regret update process, and Deep CFR, which integrates deep learning techniques and uses neural networks to estimate the regret values, enabling it to handle more complex and large-scale state spaces. Therefore, although precise methodslike CFR remain advantageous for smaller problems, particularly when accuracy is critical, heuristic methods and CFR variants (such as CFR+ and Deep CFR) provide effective alternatives for large-scale optimization tasks with more stringent time constraints. In this study, we propose a two-tier progressive model of environment-emotion and emotion-decision, where the environment influences emotions, and emotions, in turn, affect decision-making. This model captures the interconnected nature of human decision-making in dynamic environments, where emotional states are shaped by environmental factors and subsequently influence the decision-making process. We chose not to separate the layers of environment and emotion in our analysis because emotions cannot be fully decoupled from their environmental antecedents. Emotions are inherently shaped by the environment, and attempting to isolate them for independent analysis would overlook this intrinsic connection. For example, isolating emotions as an independent variable in decision-making experiments would require disregarding their dependence on environmental factors, thus potentially distorting the understanding of how emotions truly function in decision-making. Given that emotions are an essential intermediary between the environment and decision outcomes, their role is best understood within the context of the environment that induces them. Thus, in the present work, we maintain the integrated structure of our model, which reflects the natural progression from environmental stimuli to emotional response and finally to decision-making. However, we acknowledge that further research could explore the independent influences of these factors more explicitly. Future work may investigate how the environment affects decision-making directly, with a focus on the mediating role of emotions, and whether such causal relationships can be isolated in more controlled experimental settings. This direction would provide a deeper understanding of the underlying mechanisms driving decision-making in complex, dynamic contexts. Moreover, future research should focus on developing advanced dynamic emotion monitoring technologies to capture and analyze individuals' emotional changes in real-time during complex tasks.