[CALL FOR COLLABORATORS] Escaping the Middle-Income Trap: A Quantum-Inspired Machine Learning Approach to Uncovering Nonlinear Development Pathways
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| Natura: | Recurso digital |
| Lingua: | inglese |
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2025
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| _version_ | 1866901737649995776 |
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| author | Maciel, Daniel Thomaz Giacomelli Nunes |
| author_facet | Maciel, Daniel Thomaz Giacomelli Nunes |
| contents | <p><strong>Preprint - Preliminary Draft - Models Under Development</strong></p> <p><strong>[SEEKING COLLABORATORS - Contact author for collaboration opportunities]</strong></p> <p>The middle-income trap represents one of the most persistent puzzles in development economics, with only a minority of countries successfully transitioning from middle to high-income status over the past half-century.</p> <p>Traditional econometric approaches, predominantly based on linear regression frameworks, may inadequately capture the complex, high-dimensional interactions among institutional, structural, and policy variables that distinguish successful from trapped economies.</p> <p>This paper introduces a novel methodological framework leveraging quantum-inspired machine learning techniques, specifically Tensor Networks and Quantum Kernel Methods, to discover nonlinear patterns in cross-country development trajectories.</p> <p><strong>Key Contributions:</strong></p> <ul> <li>Matrix Product States decomposition for development economics</li> <li>Entanglement entropy measures mapping multidimensional correlational structures</li> <li>Identification of critical factor combinations and threshold effects</li> <li>Analysis of trapped vs. successful transitioner economies</li> </ul> <p><strong>Data Sources:</strong> World Bank, IMF, OECD, and UN databases (1990-2023)</p> <p><strong>Collaboration Invitation:</strong> Researchers interested in quantum-inspired methods, development economics, or applied econometrics are invited to contribute.</p> <p><em>Part of this content may have been AI-generated under supervision and validation by the author.</em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18304954 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | [CALL FOR COLLABORATORS] Escaping the Middle-Income Trap: A Quantum-Inspired Machine Learning Approach to Uncovering Nonlinear Development Pathways Maciel, Daniel Thomaz Giacomelli Nunes middle-income trap quantum machine learning tensor networks development economics nonlinear dynamics economic complexity structural transformation preprint call for collaborators <p><strong>Preprint - Preliminary Draft - Models Under Development</strong></p> <p><strong>[SEEKING COLLABORATORS - Contact author for collaboration opportunities]</strong></p> <p>The middle-income trap represents one of the most persistent puzzles in development economics, with only a minority of countries successfully transitioning from middle to high-income status over the past half-century.</p> <p>Traditional econometric approaches, predominantly based on linear regression frameworks, may inadequately capture the complex, high-dimensional interactions among institutional, structural, and policy variables that distinguish successful from trapped economies.</p> <p>This paper introduces a novel methodological framework leveraging quantum-inspired machine learning techniques, specifically Tensor Networks and Quantum Kernel Methods, to discover nonlinear patterns in cross-country development trajectories.</p> <p><strong>Key Contributions:</strong></p> <ul> <li>Matrix Product States decomposition for development economics</li> <li>Entanglement entropy measures mapping multidimensional correlational structures</li> <li>Identification of critical factor combinations and threshold effects</li> <li>Analysis of trapped vs. successful transitioner economies</li> </ul> <p><strong>Data Sources:</strong> World Bank, IMF, OECD, and UN databases (1990-2023)</p> <p><strong>Collaboration Invitation:</strong> Researchers interested in quantum-inspired methods, development economics, or applied econometrics are invited to contribute.</p> <p><em>Part of this content may have been AI-generated under supervision and validation by the author.</em></p> |
| title | [CALL FOR COLLABORATORS] Escaping the Middle-Income Trap: A Quantum-Inspired Machine Learning Approach to Uncovering Nonlinear Development Pathways |
| topic | middle-income trap quantum machine learning tensor networks development economics nonlinear dynamics economic complexity structural transformation preprint call for collaborators |
| url | https://doi.org/10.5281/zenodo.18304954 |