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Knowing what someone else knows is important in shared environments. However, the knowledge of others is not immediately perceptible. Luckily, an agent’s behavior within a task leaves clues for an observer to estimate their expertise. A task expert will make the best decisions to reach their goal, while a novice will make decisions seemingly at random. An expert observer should be able to evaluate the decisions of an agent by comparing the agent’s decision to the ideal one. On the other hand, a novice observer does not know the ideal decision and, thus, cannot compare the agent’s decision to it.We test the hypothesis that with increasing expertise, people can more accurately assess the expertise of others. The results of the two studies we present are consistent with this hypothesis. The first experimental study showed the expected interaction of agent and judge expertise in a task with limited external validity. In the second study, the results of the first study were replicated in a more generally applicable task. We discuss possible moderators of this effect, such as conflicting expertise cues and additional metacognitive information communicated by the agent. Moreover, we outline the consequences of expertise imbalance for identifying imposters in expert fields.
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