本研究考察基于经济复杂性（Economic Complexity）与复杂网络理论（Complex Network Theory）构建的模型对巴西市镇人类发展指数（IDHM）的预测能力。为此，将经济复杂性指数（Economic Complexity Index, ECI）适配至巴西语境。主要目标是评估区域发展分析的不同路径——例如本地生产专业化程度及在国家交通网络中的结构整合度——对社会经济发展的影响程度。方法上，比较了线性回归模型（岭回归、LASSO 和弹性网络）与非线性模型（决策树与可解释提升机，Explainable Boosting Machine），所有模型均通过五折交叉验证结合网格搜索进行评估。分析在两个空间聚合尺度上开展：市镇（municipalities）与近域地理区（Immediate Geographic Regions）。结果表明，区域聚合层面的模型统计噪声更小、稳定性更高，在解释IDHM时展现出显著更高的决定系数（R²）与更强的解释力。尤其值得注意的是，在近域地理区层面，经济复杂性指数（ICE）单独即成为IDHM的主要结构性决定因素，表明该尺度下区域生产专业化程度本身即可解释人类发展水平变异的很大一部分。此外，纳入道路基础设施网络的拓扑指标普遍提升了模型性能。其中，可解释提升机（Explainable Boosting Machine）表现最优：当纳入网络指标时，在近域地理区层面达到R² = 0.8196。

This study investigates the predictive capacity of models based on Economic Complexity and Complex Network Theory when applied to Brazil's Municipal Human Development Index (IDHM). For this purpose, the Economic Complexity Index was adapted to the Brazilian context. The main objective is to assess the extent to which different approaches to regional development analysis, such as local productive sophistication and structural integration into the national transportation network, determine socioeconomic development. The methodology compares linear regression models (Ridge, LASSO, and Elastic Net) and nonlinear models (Decision Trees and the Explainable Boosting Machine), evaluated through 5-fold cross-validation with grid search. The analyses were conducted at two levels of spatial aggregation: municipalities and Immediate Geographic Regions. The results show that regionally aggregated models exhibit less statistical noise and greater stability, achieving substantially higher coefficients of determination and greater explanatory power for ICE relative to the other variables. Notably, at the Immediate Region level, ICE alone emerges as the main structural determinant of IDHM, suggesting that regional productive sophistication by itself explains a large share of the variation in human development at this territorial scale. In addition, the inclusion of topological metrics from the road infrastructure network generally improved performance. The Explainable Boosting Machine achieved the best predictive performance, reaching R^2 = 0.8196 at the Immediate Region level when network metrics were included.