Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
We modeled human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk.
We hypothesize that the state history (e.g. rewards and decisions in previous trials) are related to the human reward function, which leads to risk-averse and risk-prone decisions. We design features that reflect these factors in the reward function of IRL and learn the corresponding weight that is interpretable as the importance of features.
The results confirm the sub-optimal risk-related decisions of human-driven by the personalized reward function. In particular, the risk-prone person tends to decide based on the current pump number, while the risk-averse person relies on burst information from the previous trial and the average end status. Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.
Reference
Liu, Q., Wu, H*. & Liu, A*. (2019). Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning, International Conference on Machine Learning,Long Beach.
Liu, Q., Wu, H*, & Liu, A*. (2019). Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning. arXiv preprint arXiv:1906.05803. . [link]