[CALL FOR COLLABORATORS] Context-Dependent Effectiveness of Social Spending and AI Policies: A Quantum-Inspired Machine Learning Approach to Public Policy Classification

Preprint - Preliminary Research Proposal - Models Under Development

[OPEN CALL FOR CO-AUTHORS - See Section 7 in the document]

The effectiveness of public policies, particularly those related to social spending and artificial intelligence (AI) investment, varies dramatically across countries and contexts. Traditional econometric approaches struggle to identify the complex, high-dimensional configurations of economic, institutional, and technological factors that determine policy success or failure.

This preprint proposes a novel methodological framework employing quantum-inspired machine learning—specifically Quantum Kernel Methods and Tensor Network classification—to map the context-dependency of policy effectiveness.

Key Contributions:

• Quantum Kernel Methods for policy effectiveness classification
• Tensor Network approach to regime-dependent policy outcomes
• Analysis of social expenditures (education, health, social protection)
• AI adoption indicators and socioeconomic outcomes analysis
• Threshold effects and complementarities identification

Data: Panel data on social expenditures and AI adoption (1990-2023)

Collaboration Invitation: We actively seek collaboration from economists, data scientists, and policy researchers to develop the full empirical implementation.

Part of this content may have been AI-generated under supervision and validation by the author.