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This paper presents a hybrid framework combining kinetic models of wealth exchange and graph convolutional networks (GCNs) to analyze economic inequality and poverty. It critiques traditional econometric models for their inability to capture the complexities of wealth distribution and proposes a methodology for simulating and predicting poverty dynamics. The framework aims to enhance policy interventions by identifying vulnerable populations through advanced data analysis techniques.

Author

kingkdb10

Language

EN