This work introduces the State Transition-Based Temporal Emergence Model (STTEM), a computational and mathematical framework that reinterprets time as an emergent property derived from ordered state transitions rather than as a fundamental independent variable. Unlike classical approaches where system evolution depends explicitly on time, the proposed model represents system dynamics through transition functions of the form xₖ₊₁ = F(xₖ), with temporal structure reconstructed as an indexing function τ(xₖ) = k. The study develops a formal mathematical formulation, a layered system architecture, and an algorithmic framework to operationalize transition-driven temporal emergence. Simulation-based analysis demonstrates that key temporal behaviors—including convergence, divergence, periodicity, and chaos—arise naturally from state evolution without requiring an explicit temporal parameter. The results establish that temporal ordering is equivalent to causal ordering, and the perceived flow of time can be interpreted as a structural progression of system states. The framework is positioned in relation to adaptive computational paradigms, particularly meta-computing systems, highlighting its relevance for modeling dynamical systems, artificial intelligence processes, and distributed environments. This work provides a unified perspective bridging computational theory and conceptual interpretations of time, suggesting that time is not a fundamental entity but a derived construct emerging from the ordering of change.
Aryan et al. (Mon,) studied this question.