This theory introduces a mechanistic model that delineates the intricate cognitive processes involved in decision-making. Drawing from psychological and [[Epistemology|epistemological ]] perspectives, the proposed model elucidates the interplay of various mental elements during the decision-making process. The components include the identification of needs, consideration of stakeholders, formulation of questions, generation of options, and the subsequent evaluation, selection, and implementation of decisions. Furthermore, the model incorporates the concept of a Mental Objects Network (MON) to elucidate the role of interconnected theories in shaping decision outcomes. The paper underscores the importance of resource analysis, value attribution to outcomes, and the nuanced consideration of probability in optimizing decision outcomes. Additionally, it explores the influence of familiarity and external factors on decision selection, emphasizing the role of learning in the development of a corroborated System of Neural ([[SON]]) essential for better predictions.