Code and data for: Decoupling learning and behavioral updating promotes cooperation in spatial public goods games

Code and data supporting the paper "Decoupling learning and behavioral updating promotes cooperation in spatial public goods games" (C. Cartes and P. Thomas). The study decouples the Q-table update (every round) from the behavioral policy update (every ρ rounds) in spatial public goods games with Q-learning agents, on regular lattice, small-world and scale-free networks (N = 10^4, T = 10^5, 30 paired replicates per condition). Contents:- sim_core.py, agents.py, game.py, networks.py: simulation code.- run_experiments.py: runs all experimental blocks (E1–E9, EF) and writes one CSV per block; resumable via per-run checkpoints. Paper scale: python run_experiments.py --scale paper --workers 10- make_figures.py: generates the nine figures of the paper from the CSV files.- results/: one CSV per block, one row per simulation run.- figures/: the nine figures in PDF.- requirements.txt: exact Python package versions used.- hashes.txt: SHA-256 checksums of the CSV files.