Modeling And Interpreting Real-world Human Risk Decision Making With Inverse Reinforcement Learning

Abstract

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

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyliu2019modeling

Related papers

• Active Learning For Risk-sensitive Inverse Reinforcement Learning (2019)0.00
• Misspecification In Inverse Reinforcement Learning (2022)5.24
• Model Selection For Inverse Reinforcement Learning Via Structural Risk Minimization (2023)0.00
• Reward Shaping For Human Learning Via Inverse Reinforcement Learning (2020)0.00
• Accounting For Human Learning When Inferring Human Preferences (2020)0.00
• Partial Identifiability And Misspecification In Inverse Reinforcement Learning (2024)0.00
• Towards Theoretical Understanding Of Inverse Reinforcement Learning (2023)0.00
• Distributional Inverse Reinforcement Learning (2025)0.00