Novel Approach to Modeling Investor Decision-Making Using the Dual-Process Theory: Synthesizing Experimental Methods from Within-Subjects to Between-Subjects Designs

This paper addresses a central contradiction in dual-process theories of reasoning: identical tasks produce different outcomes under within-subjects and between-subjects experimental designs. Drawing on two prior studies that exemplify this divergence, we synthesize the empirical patterns into a unified theoretical account. We propose a conceptual framework in which the research design itself serves as a cognitive moderator, influencing the dominance of System 1 (intuitive) or System 2 (analytical) processing. To formalize this synthesis, we introduce a mathematical model that captures the functional relationship between methodological framing, cognitive system engagement, and decision accuracy. The model supports both forward prediction and Bayesian inference, offering a scalable foundation for future empirical calibration. This integration of experimental design and cognitive processing contributes to resolving theoretical ambiguity in dual-process research and opens avenues for predictive modeling of reasoning performance. By formalizing dual-process cognition through dynamic system analogies, this study contributes a continuous modeling approach to performance fluctuations under methodological asymmetry.