Structural Emergence of Intelligence (SEI) v2.4: From Structural Conditions to Empirical Decision Pathways

We present SEI v2. 4 (Structural Emergence of Intelligence), a minimal and falsifiable framework describing how intelligence emerges across natural and artificial systems. The central claim is that intelligence does not arise from scale alone, but emerges only within a bounded structural regime defined by: C⋅Γ⋅dSCdt>Θ (E, S, T) C dSCdt > (E, S, T) C⋅Γ⋅dtdSC>Θ (E, S, T) where: CCC: effective structural density ΓΓ: fixation / stabilization dSC/dtdSC/dtdSC/dt: persistence of structural organization Θ (E, S, T) (E, S, T) Θ (E, S, T): context-dependent emergence threshold Key Contributions in v2. 4 Introduction of an explicit empirical decision framework for validation or falsification Formalization of the effective ratio: R (E) =C⋅Γ⋅dSCdtΘ (E) R (E) = C dSC{dt} (E) R (E) =Θ (E) C⋅Γ⋅dtdSC Identification of a non-monotonic emergence regime (bounded optimum) Extension to time-evolving structural emergence windows R (E, T) R (E, T) R (E, T) Integration of cross-scale mapping across galactic, planetary, biological, neural, and AI systems Clear falsifiability criterion: if intelligence scales monotonically without a bounded optimum, the framework is weakened or falsified Empirical Interpretation SEI v2. 4 introduces a minimal pathway from observation to theory testing: Estimate R (E) R (E) R (E) from observed scaling data Detect presence or absence of a bounded peak regime Use this as a direct criterion for supporting or rejecting the framework Significance This work shifts intelligence from a scale-driven interpretation to a structurally constrained emergence phenomenon, providing: A unified structural language across domains A directly testable prediction framework A bridge between theoretical formulation and empirical validation All figures are fully reproducible using the provided Python scripts.