Monte Carlo Simulation Results

The Monte Carlo Simulation Results are the book's statistical projection, from 10⁵ simulated runs, of when computational evolution converges on the recursive intelligence threshold, given as a probability distribution with confidence intervals. The simulations extend the recursive scaling laws of computing history into the future, combining exponential growth with recursive amplification factors and sampling the uncertainty in amplification strength and transition timing. The outcome is a normal distribution for the convergence year, which the book bases on patterns across computational epochs, with mean 2037.2 and standard deviation 3.8 years. The confidence intervals are 2033.4 to 2041.0 at 68%, 2029.8 to 2044.6 at 95% and 2027.4 to 2047.0 at 99%. The book reads these as probabilistic trajectories that give bounded foresight into quantum supremacy and recursive intelligence milestones. The distribution is the Timeline Probability Distribution P(T⋒) = (1/(σ_T × √(2π))) × exp(−(T − μ_T)²/(2σ_T²)), where T is the convergence time, μ_T the mean convergence time (2037.2) and σ_T the standard deviation (3.8 years). Dimensions: P [𝕋⁻¹], T, μ_T, σ_T [𝕋]; the book's check is consistent. It connects to the Timeline Probability Distribution, Timeline Convergence, Historical Scaling Across Computational Epochs, Historical Bit Curve and Timeline to Singularity.