The Neural Mechanisms of Learning to Balance Fairness and Self-Interest: A Reinforcement Learning Account

Using a computational modeling approach applying reinforcement learning to Ultimatum Bargaining we found that individuals learn faster and earn more wages when playing against computer generated lotteries than when playing against human responders with identical response patterns. This suggests that learning to trade efficiently is hindered by fairness norms.