This dataset contains the data and code to reproduce all empirical and computational results in the manuscript "The Coevolution of Technology and Policy" (Stauffer, 2026, submitted to Science Advances). The study introduces an empirical-mechanistic framework that constructs domain-specific technology and policy indices (2002-2024) using moving dynamic principal component analysis (mDPCA) and calibrates an agent-based model via Approximate Bayesian Computation (ABC) to reproduce observed process signatures across four technology domains: renewable energy, electric vehicles, information and communication technology, and artificial intelligence. Contents: Data/ - Domain-specific datasets (renewables, EV, ICT, AI) as ZIP archives containing processed CSV files Code/ - Python scripts for index construction (mDPCA), acceleration/extreme-event analysis, higher-order interactions, and ABM pipeline (ABC calibration + validation) README.md - Full reproduction instructions Simulations are fully reproducible through seeded random number generation (Python >= 3.10).

Related manuscript: Stauffer, M. (2026). The Coevolution of Technology and Policy. Science Advances (submitted).