Convergent Discovery of Critical Phenomena Mathematics Across Disciplines: A Cross-Domain Analysis

Abstract: Techniques for detecting critical phenomena -- phase transitions where correlation length diverges and small perturbations have large effects -- have been developed across at least eight fields of application over nine decades. We document this convergence pattern. The physicist's correlation length $ξ$, the cardiologist's DFA scaling exponent $α$, the financial analyst's Hurst exponent $H$, and the machine learning engineer's spectral radius $χ$ all measure correlation decay rate, detecting the same critical signatures under different notation. Citation analysis reveals minimal cross-domain awareness during the formative period (1987--2010): researchers in biomedicine, finance, machine learning, power systems, and traffic flow developed equivalent techniques independently, each with distinct notation and terminology. We present Metatron Dynamics, a framework derived from distributed systems engineering, as a candidate ninth independent discovery -- strengthening the convergence pattern while acknowledging that as authors of both the framework and this analysis, external validation would strengthen this claim. Correspondence testing on the 2D Ising model confirms that measures from multiple frameworks correctly identify the critical regime at $T_c = 2.269$. We argue that repeated independent discovery establishes criticality mathematics as fundamental public knowledge, with implications for cross-disciplinary education and research accessibility. Because these findings affect fields beyond mathematics and physics, we include a plain-language summary in Appendix B for non-specialist readers.