Neurologistics Simulation Code: Neuro-Inspired Forecasting of Ethical Decision-Making in Sustainable Supply Chains

This dataset contains the full Python codebase used in the study "Neurologistics: A Neuro-Inspired Simulation for Forecasting Ethical Decision-Making in Sustainable Supply Chains." The Neurologistics framework models how biologically inspired cognitive traits—empathy, stress tolerance, and impulse control—influence ethical trade-offs in logistics operations under pressure from emerging technologies (e.g., AI optimization, autonomous delivery, ESG dashboards). The simulation produces 15,000 agent-based decisions across three ethical scenarios: speed vs. emissions, cost vs. labor rights, and efficiency vs. sustainability. It includes modules for simulation execution, econometric analysis, structural equation modeling, robustness checks, and visualizations. Several files simulate learning mechanisms and compare agent behavior across time, enabling the study of ethical adaptation. The code supports generating figures in the manuscript and allows replication of all reported findings.