This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The proposed framework leverages structure-preserving embeddings and graph-informed coupling matrices to model inter-agent dynamics with enhanced interpretability. A constrained optimization scheme ensures that the learned models satisfy both stochastic and structural constraints. Two empirical case studies, a dynamic behavioral model of r...