arXiv subjects

Ngueuleweu Tiwang Gildas

Publications and source records attributed to Ngueuleweu Tiwang Gildas.

3 recordsLinked to original sources

arXiv2026

General Theory of Relational Primacy

This paper presents the General Theory of Relational Primacy (GTRP), a conceptual and formal framework for understanding stability, crisis, and transition in complex systems. The central thesis is that systems are not defined by their variables but by the relations that organize them. Variables are merely late manifestations of deep relational mechanisms. Crises arise from the silent reorganization of these relations, not from changes in the variables themselves. We formalize three axioms (relational primacy, antecedence of relations, and universality of coupling) and derive from them a complete hypothetico-deductive framework (hypotheses H1-H5, validity conditions C1-C6, and properties P1-P5 of the observable beta). We prove a foundational theorem establishing that every complex system possesses an irreducible relational level, introduce an invariant of the theory analogous to the conservation of energy, and propose universal definitions of crisis, relational causality, and relational information. The parameter beta(t) = d log y / d log x, estimated via a time-varying Kalman filter in the log-log space, measures this reorganization with an O(delta theta) sensitivity, one order of magnitude faster than classical O((delta theta)^2) signals. The empirical evidence (NASA AIRS data, 2002-2026) and validations on six simulated systems confirm that beta precedes classical early warning signals by 14 to 24 months and 39 to 153 time steps. The theory is designed to be universal, applicable to climate, biology, economics, artificial intelligence, and sovereignty of systems.

econ.TH↗

arXiv2026

Mapping the Energetic Structure of Climate Transitions for Policy Relevant Regime Detection

Understanding how climate and innovation policies perform during socio-technical transitions remains a central challenge in innovation studies. Empirical analyses of the relationship between economic growth and carbon emissions continue to yield conflicting results, partly because they rely on pooled models that implicitly assume stable and homogeneous dynamics. Transition theory, by contrast, emphasizes that decarbonization unfolds through heterogeneous regimes characterized by varying degrees of stability, inertia, and reconfiguration. Yet, empirical tools capable of identifying these regimes prior to policy evaluation or forecasting remain limited. This paper introduces a regime-diagnostic framework designed to condition empirical analysis on the structural state of the climate-economy system. Rather than estimating causal effects or generating forecasts directly, the framework reconstructs latent transition regimes from the time varying responsiveness of emissions to economic activity. These diagnostics are used as a pre-modeling step, allowing econometric and machine learning tools to be applied conditionally on empirically identified regimes. Using a global panel of approximately 150 countries over the period 1991-2022, we apply the framework to the emission-growth relationship.

econ.TH↗

arXiv2025

A Hamiltonian Higher-Order Elasticity Framework for Dynamic Diagnostics(2HOED)

Machine learning detects patterns, block chain guarantees trust and immutability, and modern causal inference identifies directional linkages, yet none alone exposes the full energetic anatomy of complex systems; the Hamiltonian Higher Order Elasticity Dynamics(2HOED) framework bridges these gaps. Grounded in classical mechanics but extended to Economics order elasticity terms, 2HOED represents economic, social, and physical systems as energy-based Hamiltonians whose position, velocity, acceleration, and jerk of elasticity jointly determine systemic power, Inertia, policy sensitivity, and marginal responses. Because the formalism is scaling free and coordinate agnostic, it transfers seamlessly from financial markets to climate science, from supply chain logistics to epidemiology, thus any discipline in which adaptation and shocks coexist. By embedding standard econometric variables inside a Hamiltonian, 2HOED enriches conventional economic analysis with rigorous diagnostics of resilience, tipping points, and feedback loops, revealing failure modes invisible to linear models. Wavelet spectra, phase space attractors, and topological persistence diagrams derived from 2HOED expose multistage policy leverage that machine learning detects only empirically and block chain secures only after the fact. For economists, physicians and other scientists, the method opens a new causal energetic channel linking biological or mechanical elasticity to macro level outcomes. Portable, interpretable, and computationally light, 2HOED turns data streams into dynamical energy maps, empowering decision makers to anticipate crises, design adaptive policies, and engineer robust systems delivering the predictive punch of AI with the explanatory clarity of physics.

cs.LG↗