Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
This provides a tool for interpreting human psychological processes in risk-taking, but it is incremental as it applies an existing method to a new domain.
The paper tackled modeling human risk decision-making by applying inverse reinforcement learning (IRL) to reveal implicit reward functions, showing that risk-prone individuals focus on current pump numbers while risk-averse ones rely on previous burst information and average end status.
We model 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. To the best of our knowledge, this is the first work applying IRL to reveal the implicit reward function in human risk-taking decision making and to interpret risk-prone and risk-averse decision-making policies. 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.