Recursive Modelling of Reality: A Self-Referential Framework Integrating Information Theory, Dynamical Systems, and Artificial Intelligence

Contemporary scientific models effectively describe complex physical and computational dynamics but typically maintain a separation between observer and observed system, limiting explanatory coherence in self-referential and adaptive systems. This paper proposes a recursive modelling framework in which system evolution depends on feedback between system states and internally generated representations. Drawing on information theory, nonlinear dynamics, and complex systems modelling, the study introduces the principle of recursive necessity, whereby stability emerges through self-consistent interaction between state and self-model. Physical states are interpreted as stabilized informational configurations sustained through recursive dynamics. The framework provides a unified modelling perspective linking chaos, self-organization, and adaptive control, while suggesting implications for artificial intelligence architectures incorporating persistent self-representation beyond optimization-based learning. Rather than replacing existing models, the approach extends current modelling paradigms by integrating selfreference as a structural component of dynamical systems. The proposed framework offers a theoretical basis for studying emergence, intelligence, and stability in complex adaptive environments.