Artificial Intelligence as a Foundation for Enhanced Theoretical Macroeconomic Modeling: Integrating Political, Social, and Cultural Realities in the Brazilian Context

Abstract This article explores the application of artificial intelligence (AI) techniques to enhance macroeconomic modeling by systematically incorporating political, social, and cultural dimensions in the Brazilian context. Traditional macroeconomic models often treat non-economic factors as exogenous or peripheral, limiting their explanatory power in complex emerging economies. We develop an integrated AI-enhanced framework that leverages machine learning, network analysis, and deep reinforcement learning to capture the multidimensional interdependencies that characterize Brazil's economic dynamics. Using synthetic data calibrated to Brazilian economic patterns, we demonstrate that our approach achieves superior forecasting accuracy compared to traditional models, with a 70.6% improvement in RMSE over ARIMA and a 23.1% improvement over VAR models. Feature importance analysis reveals that variables from all four dimensions—economic, political, social, and cultural—contribute significantly to predictive performance, with risk aversion, inequality, and political stability emerging as particularly influential. Policy simulations indicate that our integrated approach enables more effective policy design, achieving better outcomes across both economic metrics and social indicators. Regional analysis highlights the importance of accounting for Brazil's significant regional disparities, with model performance varying based on local human capital, inequality, and cultural attitudes. Our findings demonstrate the value of AI-enhanced multidimensional modeling for understanding complex emerging economies and designing more effective policies that account for the rich tapestry of factors that shape economic outcomes.