  To achieve desirable performance, current AI systems often require huge
amounts of training data. This is especially problematic in domains where
collecting data is both expensive and time-consuming, e.g., where AI systems
require having numerous interactions with humans, collecting feedback from
them. In this work, we substantiate the idea of $\textit{cognitive models as
simulators}$, which is to have AI systems interact with, and collect feedback
from, cognitive models instead of humans, thereby making their training process
both less costly and faster. Here, we leverage this idea in the context of
moral decision-making, by having reinforcement learning (RL) agents learn about
fairness through interacting with a cognitive model of the Ultimatum Game (UG),
a canonical task in behavioral and brain sciences for studying fairness.
Interestingly, these RL agents learn to rationally adapt their behavior
depending on the emotional state of their simulated UG responder. Our work
suggests that using cognitive models as simulators of humans is an effective
approach for training AI systems, presenting an important way for computational
cognitive science to make contributions to AI.
