Integrating emotional and cognitive biases in graded decision-making models: Insights from a theoretical case study in healthcare

Classical decision theory has long been anchored in the ideal of the rational actor, guided by internally consistent preferences and the axioms of Expected Utility Theory (EUT). Behavioral economics and cognitive psychology have enriched this framework by revealing systematic deviations driven by cognitive biases, while affective science has exposed the profound role of emotional states in shaping perception, valuation, and choice. Yet, in most formal models, cognition and emotion remain analytically segregated, treated as distinct modifiers rather than dynamically coupled determinants of behavior. This paper introduces the Integrated Behavioral Decision-Making Model (IBDM), a formal, parameterized framework that unifies graded emotional states and cognitive distortions into a single utility structure. Drawing inspiration from Vernon L. Smith's reinterpretation of Adam Smith's Theory of Moral Sentiments, the IBDM incorporates the boundedness of human rationality not as a deviation from a normative standard, but as a structural feature of decision-making. Fear and optimism are modeled as graded intensities that interact multiplicatively with cognitive biases such as loss aversion and anchoring, altering decision utility in nonlinear and path-dependent ways. A theoretical case study in healthcare decision-making, whether to undergo high-risk surgery, is simulated using Python. Results demonstrate that small changes in emotional intensity can reverse predicted preferences under traditional models, revealing regions of decision-space where affective forces dominate over risk-return trade-offs. This insight reframes the design of interventions in personalized medicine and public health, highlighting the necessity of emotionally intelligent policy tools. The IBDM is not merely a theoretical innovation: it is an epistemic bridge between normative theory, empirical behavioral evidence, and the lived emotional realities of decision contexts. It provides a blueprint for experimental validation, cross-disciplinary integration, and the next generation of computational behavioral models.