Recursive introversion, iterative extroversion and transitive ambiversion

This study proposes a theoretical formalization of personality dynamics in which introversion, extroversion, and ambiversion are modeled as recursion, iteration, and transitivity, respectively. Rather than treating these phenomena as fixed traits, the article interprets them as interacting processual regimes unfolding within a nonlinear and multilayered system. The model is grounded in complex systems theory, nonlinear dynamics, and a formal vocabulary designed to describe the interplay between internal reprocessing, external responsiveness, and adaptive integration. The contribution of the article is conceptual and formal rather than empirical. To prevent the formal model from remaining entirely detached from observable data, the article also includes a small exploratory empirical anchoring based on an anonymous survey. This provides indirect but nonconfirmatory support for the operational distinction between recursive, iterative, and transitive personality-process indicators. It derives analytical expressions for the interaction of internal and external factors and clarifies the interpretive role of key parameters, including sensitivity coefficients, weighting factors of stimuli, and synergy parameters. These quantities are introduced as structural and context-sensitive variables, not as fixed psychological constants. Accordingly, the present work does not claim psychometric validation, direct diagnostic readiness, or established forecasting power. Its significance lies in offering a mathematically explicit framework that may guide future empirical testing, simulation studies, and domain-specific operationalization in psychology, education, organizational research, and related fields. Formalization provides a path toward quantitative personality research while remaining fully aware of its present limits. It provides a theoretical basis for further interdisciplinary work at the intersection of psychology, the cognitive sciences, systems theory, and computational modeling. In this sense, the proposed framework should be understood not as a finished applied instrument but as a first formal language within which future research may investigate behavioral archetypes, regime transitions, and algorithmic approximations of differentiated information-processing styles in human and artificial agents.