This paper introduces the ΔΦ Law of Intelligent Emergence, a field-based framework that replaces stochastic evolutionary mechanisms with electrostatic gradient-driven resolution. Using simulations grounded in the Mitchell Equation (ΔΦ = ρ × v), the study demonstrates that systems aligned with normalized gradients exhibit sustained convergence toward structured attractors, while random systems show rapid but shallow error minimization followed by stagnation. The results establish Electrostatic Gradient Selection (EGS) as a falsifiable and measurable substrate for emergence across biological and computational systems. This work redefines intelligence as sustained directional coherence rather than instantaneous optimization.
Thomas S. Mitchell (Tue,) studied this question.