  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.
