| _version_ | 1866901057079083008 |
|---|---|
| author | Ngueuleweu Tiwang, Gildas |
| author_facet | Ngueuleweu Tiwang, Gildas |
| contents | <p>Machine learning detects patterns, blockchain guarantees trust and<br>immutability, and modern causal inference identifies directional linkages, yet<br>none alone exposes the full energetic anatomy of complex systems; the Hamil<br>tonian Higher Order Elasticity Dynamics (2HOED) framework bridges these gaps.<br>Grounded in classical mechanics but extended to Economics order elasticity<br>terms, 2HOED represents economic, social, and physical systems as energy based<br>Hamiltonians whose position, velocity, acceleration, and jerk of elasticity jointly<br>determine systemic power, Inertia, policy sensitivity, and marginal responses. Be<br>cause the formalism is scale free and coordinate agnostic, it transfers seamlessly<br>from financial markets to climate science, from supply chain logistics to epidemi<br>ology—any discipline in which adaptation and shocks coexist. By embedding<br>standard econometric variables inside a Hamiltonian, 2HOED enriches conven<br>tional economic analysis with rigorous diagnostics of resilience, tipping points,<br>and feedback loops, revealing failure modes invisible to linear models. Wavelet<br>spectra, phase space attractors, and topological persistence diagrams derived from<br>2HOED expose multiscale policy leverage that machine learning detects only<br>empirically and blockchain secures only after the fact. For economists, physicians<br>and other scientists, the method opens a new causal energetic channel linking bi<br>ological or mechanical elasticity to macro level outcomes. Portable, interpretable,<br>and computationally light, 2HOED turns data streams into dynamical energy<br>maps, empowering decision makers to anticipate crises, design adaptive policies,<br>and engineer robust systems—delivering the predictive punch of AI with the<br>explanatory clarity of physics. An illustration using the Kutznet environmental<br>theory on the relationship between CO2 emissions and GDP growth is applied for<br>illustration. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15299938 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Hamiltonian Higher-Order Elasticity Frame work for Dynamic Diagnostics(2HOED) Ngueuleweu Tiwang, Gildas Econometrics Physics Environmental sciences <p>Machine learning detects patterns, blockchain guarantees trust and<br>immutability, and modern causal inference identifies directional linkages, yet<br>none alone exposes the full energetic anatomy of complex systems; the Hamil<br>tonian Higher Order Elasticity Dynamics (2HOED) framework bridges these gaps.<br>Grounded in classical mechanics but extended to Economics order elasticity<br>terms, 2HOED represents economic, social, and physical systems as energy based<br>Hamiltonians whose position, velocity, acceleration, and jerk of elasticity jointly<br>determine systemic power, Inertia, policy sensitivity, and marginal responses. Be<br>cause the formalism is scale free and coordinate agnostic, it transfers seamlessly<br>from financial markets to climate science, from supply chain logistics to epidemi<br>ology—any discipline in which adaptation and shocks coexist. By embedding<br>standard econometric variables inside a Hamiltonian, 2HOED enriches conven<br>tional economic analysis with rigorous diagnostics of resilience, tipping points,<br>and feedback loops, revealing failure modes invisible to linear models. Wavelet<br>spectra, phase space attractors, and topological persistence diagrams derived from<br>2HOED expose multiscale policy leverage that machine learning detects only<br>empirically and blockchain secures only after the fact. For economists, physicians<br>and other scientists, the method opens a new causal energetic channel linking bi<br>ological or mechanical elasticity to macro level outcomes. Portable, interpretable,<br>and computationally light, 2HOED turns data streams into dynamical energy<br>maps, empowering decision makers to anticipate crises, design adaptive policies,<br>and engineer robust systems—delivering the predictive punch of AI with the<br>explanatory clarity of physics. An illustration using the Kutznet environmental<br>theory on the relationship between CO2 emissions and GDP growth is applied for<br>illustration. </p> |
| title | A Hamiltonian Higher-Order Elasticity Frame work for Dynamic Diagnostics(2HOED) |
| topic | Econometrics Physics Environmental sciences |
| url | https://doi.org/10.5281/zenodo.15299938 |