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

• Published in: Mathematics (Basel) Vol. 13; no. 19; p. 3090
• Main Authors: Lipshits, Rachel, Goldstein, Kelly, Goldstein, Alon, Eichel, Ron, Goldstein, Ayelet
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26.09.2025
• Subjects: Bayesian analysis; Bayesian modeling; Binomial distribution; Cognition; Cognition & reasoning; conceptual replication; Decision making; Design of experiments; dual-system theory; Dynamical systems; experimental economics; Investigations; judgment and decision-making; Mathematical models; Prediction models; Probability; rationality and bias; Reasoning; Statistical inference; Synthesis
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math13193090

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.