Modelling stock-market investors as Reinforcement Learning agents

Decision making in uncertain and risky environments is a prominent area of research. Standard economic theories fail to fully explain human behaviour, while a potentially promising alternative may lie in the direction of Reinforcement Learning (RL) theory. We analyse data for 46 players extracted from a financial market online game and test whether Reinforcement Learning (Q-Learning) could capture these players behaviour using a riskiness measure based on financial modeling. Moreover we test an earlier hypothesis that players are “naíve” (short-sighted). Our results indicate that Reinforcement Learning is a component of the decision-making process. We also find that there is a significant improvement of fitting for some of the players when using a full RL model against a reduced version (myopic), where only immediate reward is valued by the players, indicating that not all players are naíve.