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We model human decision-making behaviors in a risk-taking task using inversereinforcement learning (IRL) for the purposes of understanding real humandecision making under risk. To the best of our knowledge, this is the firstwork applying IRL to reveal the implicit reward function in human risk-takingdecision making and to interpret risk-prone and risk-averse decision-makingpolicies. We hypothesize that the state history (e.g. rewards and decisions inprevious trials) are related to the human reward function, which leads torisk-averse and risk-prone decisions. We design features that reflect thesefactors in the reward function of IRL and learn the corresponding weight thatis interpretable as the importance of features. The results confirm thesub-optimal risk-related decisions of human-driven by the personalized rewardfunction. In particular, the risk-prone person tends to decide based on thecurrent pump number, while the risk-averse person relies on burst informationfrom the previous trial and the average end status. Our results demonstratethat IRL is an effective tool to model human decision-making behavior, as wellas to help interpret the human psychological process in risk decision-making.

Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning