Moral decision-making involves a complex interplay between cognitive reasoning and emotional forces. This paper presents a theoretical model that formalizes moral decision processes using a sigmoid function to balance conative (driving) and inhibitory (restraining) forces. Each force is represented as a vector whose intensity is quantified on a standardized scale, allowing the model to compute an emotional score E. The probability of action is then determined by applying a sigmoid transformation to this score.The model is illustrated through case studies, including Heinz's Dilemma and The Betrayed Husband Scenario, demonstrating how emotional and contextual factors influence decision-making. The paper also explores the empirical calibration of the sensitivity parameter k, which modulates decision reactivity, and discusses the limitations of linear addition in capturing non-linear and cumulative emotional effects.Additionally, we propose an extension incorporating priming effects and contextual variables, such as social pressure or temporal influences. Future research directions include empirical validation through psychophysiological measurements (EEG, fMRI) and the integration of dynamic temporal adjustments to refine predictive accuracy. This approach aims to bridge the gap between affective-cognitive mechanisms and quantifiable decision models, with applications in moral psychology, behavioral sciences, and artificial intelligence ethics.