A deep neural network to find Nash equilibria of normal-form stage games
Code built for the testing and analysis of mixed optimization criteria in multi-objective multi-agent reinforcement learning
A Python library that simulates agent interactions in normal-form games, uncovering how strategies evolve and adapt over time.
AMPL code for computing a Proper Equilibrium of a 2 players, zero-sum game in normal form. Using implementation from Miltersen, P. B., & Sørensen, T. B. (2006). Computing proper equilibria of zero-sum games.
A Python library for simulating two‑player strategic interactions with memory, shocks, and recovery dynamics. It supports large‑scale Monte Carlo experiments to study how behavioral norms form, adapt, and regain stability after temporary disruptions.
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