The GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP.

翻译：国家的GDP被建模为两个主体间的相对交互——反映人口社会选择的工作时长，以及反映集体对生产力提升因素投入的全要素生产率。研究表明，随机森林模型能够基于这两个因素准确预测GDP。通过基尼重要性、SHAP图和部分依赖性分析，揭示了德国与美国在社会选择上的差异。结果表明，两国社会结构的差异体现在工作时长和生产力对GDP的相对贡献中。