AI-Driven Framework for Poverty Alleviation through Behavioral Heterogeneity and Economic Action Sets

This work introduces a novel AI-driven framework for poverty alleviation that leverages behavioral heterogeneity and the concept of Economic Action Sets (EAS). Traditional policies often assume homogeneous responses, overlooking individual differences in cognitive traits, risk preferences, and self-control. Our model uses high-precision AI profiling to dynamically tailor interventions, expanding feasible economic choices for each individual and disrupting behavioral poverty traps. Simulation results suggest personalized, adaptive interventions can reduce urban poverty rates by 20–25% over five years, outperforming standard uniform-transfer approaches. The framework is scalable, cost-efficient, and ethically guided, emphasizing human agency and predictive, data-driven policy design. Keywords: AI, Poverty Alleviation, Behavioral Heterogeneity, Economic Action Set, Personalized Policy, Social Mobility, Adaptive Intervention License: CC BY 4.0