In traditional Reinforcement Learning (RL), agents learn to optimize actions in a dynamic context based on recursive estimation of expected values. We show that this form of machine learning fails when rewards (returns) are affected by tail risk, i.e., leptokurtosis. Here, we adapt a recent...

The Lucas asset pricing model is studied here in a controlled setting. Participants could trade two long-lived securities in a continuous open-book system. The experimental design emulated the stationary, infinite-horizon setting of the model and incentivized participants to smooth consumption...