This paper focuses on the intersection of affective and analytical directed acyclic graphs (DAGs) in the context of Decision Support Systems (DSS). Topics explored in this study are Decision Theory Models in the areas of Affective Computing and Bayesian Decision Theory. The research analyzes how these approaches can be implemented under the proposed Affective-Analytic Decision Framework (AADF) using Information Fusion and Human-Centered Design. To improve transparency and trust, Explainable Artificial Intelligence (XAI) is proposed in the Affective-Analytic Decision Framework (AADF) framework to merge logic and analytic models with empathetic insights into affective DAGs. For this paper, the research emphasis is placed on analyzing Bayesian networks and Markov models which offer probabilistic techniques during uncertainty in decision making. Ideally, including affect into analytic models will ensure algorithms can increase user trust. By including emotional states and the user’s experience, the goal is to develop emotionally intelligent A.I. systems that take into consideration emotion during the decision-making process.