When Learning Goes Rogue: Fixing RL Biases in Economic Simulations
Reinforcement Learning (RL) has become a seductive tool for economists seeking to simulate adaptive behavior in dynamic, uncertain environments. But when it comes to modeling firms in equilibrium labor markets, this computational marriage reveals some serious incompatibilities. In a recent paper, Zhang and Chen expose two critical mismatches that emerge when standard RL is naively applied to simulate economic models — and offer a principled fix that merges the best of RL and economic theory. ...