## 1 Introduction
Therefore, Kuhn poker was selected for its balance of simplicity, strategic depth, and relevance to the study of risk preferences and cooperation under the influence of immediate emotions. Its tractable structure allows for precise control and analysis of decision-making dynamics, making it an ideal framework for examining the interplay between emotions, risk-taking, and human-machine collaboration. Nevertheless, most current experiments employ static emotion-induction methods (e.g., short videos or questionnaires), ignoring the dynamic changes in emotions during the experiment and the continuous regulatory effects of environmental factors [16]. Such experimental designs diverge from real-world dynamic emotional variations, leading to lower external validity [17; 18]. Additionally, existing human-machine decision support systems are often based on the \"rational agent\" assumption, emphasizing algorithmically generated rational suggestions while overlooking individual behavioral deviations due to immediate emotional changes and differences in adoption willingness, thus limiting the practical applicability of these systems. To tackle these challenges and fill the gap in the literature, in this paper, we propose an innovative approach by incorporating environmental variables into the emotion process, constructing a two-tiered progressive inquiry environment-emotion and emotion-decision model. Utilizing the classic game scenario of Kuhn poker and Counterfactual Regret Minimization (CFR) algorithms [19; 20], we developed an intelligent decision support system. By dynamically regulating the experimental environment, our study systematically reveals the dynamic impact of immediate emotions on participants' risk preferences and cooperation tendencies. The core objective is to elucidate the regulatory role of immediate emotions within intelligent assistance systems through the \"two-tiered progressive model\", providing new insights and empirical support for the design of emotion-driven intelligent decision systems. The main contributions of this paper are summarized in the following. * We introduce a novel environment-emotion and emotion-decision model to systematically investigate how environmental factors affect emotion in human-computer interaction systems and hence decision-making behavior. This model addresses the limitations of traditional approaches by focusing on short-term emotional dynamics. * We develop an intelligent decision support system based on Kuhn poker and Counterfactual Regret Minimization algorithms. This system provides real-time strategic advice and examines how immediate emotions affect participants' willingness to adopt AI recommendations, enhancing human-machine collaboration. * Through an experiment with 80 participants, this study empirically validates the significant influence of immediate emotions on risk preferences and cooperation tendencies within human-machine interactive systems. The remainder of this paper is organized as follows: Section 2 provides a brief review of related work and highlights the novelty of this paper. Section 3 introduces the materials and methods in Kuhn poker, incorporating the two ways of environment-emotion and emotion-decision. Simulation results are presented in Section 4, followed by discussion and conclusions in Sections 5 and 6.