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To achieve desirable performance, current AI systems often require hugeamounts of training data. This is especially problematic in domains wherecollecting data is both expensive and time-consuming, e.g., where AI systemsrequire having numerous interactions with humans, collecting feedback fromthem. In this work, we substantiate the idea of $\textit{cognitive models assimulators}$, which is to have AI systems interact with, and collect feedbackfrom, cognitive models instead of humans, thereby making their training processboth less costly and faster. Here, we leverage this idea in the context ofmoral decision-making, by having reinforcement learning (RL) agents learn aboutfairness 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 behaviordepending on the emotional state of their simulated UG responder. Our worksuggests that using cognitive models as simulators of humans is an effectiveapproach for training AI systems, presenting an important way for computationalcognitive science to make contributions to AI.

Cognitive Models as Simulators: The Case of Moral Decision-Making