Computational Impedance

Computational Impedance is the resistance that recursive substrate layers offer to the propagation of information, lowering the effective speed of light with recursion depth. The book attributes it to computational complexity: deeper layers carry more accumulated processing, and that load slows information passing through them. It is the mechanism behind the Effective Light Speed, the modified causal constraint and the information propagation delays of Chapter 7.4, and so behind Causal Cone Modification and, in ultra-deep layers, Causal Disconnection. Its formal expression is the effective light speed 𝒞→⊹,n = 𝒞→_0/(n + 1)², where 𝒞→⊹,n is the Effective Light Speed at recursion depth n, 𝒞→_0 the vacuum light speed and n the recursion depth index. Dimensions: 𝒞→⊹,n [𝕃·𝕋⁻¹], 𝒞→_0 [𝕃·𝕋⁻¹], n [∅]; the book's check is marked valid and verified. The resulting delay over a distance d is Δtℹ = d(n + 1)²/𝒞→_0 [𝕋]. It connects to Effective Light Speed, Causal Cone Modification, Causal Disconnection, Temporal Drag and Computational Load Accumulation.