This pioneering framework assesses stability in chaotic systems, indicating new insights for interdisciplinary applications.
This study presents Systemic Tau (τs), a pioneering universal met-ric designed to assess stability across chaotic systems, emerging from2022 doctoral fieldwork in Puerto Rico’s Caño Martín Peña, where or-dinal rankings of fluctuating Aedes aegypti populations unveiled hidden order amidst complexity [Padilla-Villanueva, 2022]. Rooted in a novel conceptualization of time as discrete event conjunctions—guidedby Feigenbaum constants (δ ≈4.669, α ≈2.502)—τs synthesizes ordinal correlations and fractal self-organization, measuring the average Kendall’s tau across multisite time series. During stable phases, τs typically ranges from 0.5 to 0.6, with variance σ2 ≤ 0.05 across 1000-iteration simulations, while it declines below 0.41 during bifurcations,marking critical phase transitions tied to discrete events. This framework, validated through extensive empirical datasets, harmonizes chaos analysis, artificial intelligence, and climate modeling, offering a robust tool for interdisciplinary applications. Simulations further reveal τs≈0.036 beyond the Feigenbaum point, showcasing 15% noise tolerance and variance constraints (σ2 ≤1/N), with topological resilience enhanced by graph-weighted networks. By reconciling discrete and continuous time through renormalization group theory, τs surpasses conventional methods like polynomial chaos expansions, uncovering underlying order in chaotic dynamics. Its versatility is demonstrated across diverse domains, with practical implications for predicting climate extremes, optimizing AI training stability, and enhancing ecological resilience. Moreover, the study addresses ethical considerations, emphasizing responsible deployment in vulnerable communities, such as those in San Juan, to mitigate socioeconomic impacts of instability. This paradigm not only advances theoretical understanding butalso provides actionable insights for real-world challenges in an era ofinterdisciplinary discovery.
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Jaime Padilla (2025) studied this question.
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