  The ability to interpret the mental state of another agent based on its
behavior, also called Theory of Mind (ToM), is crucial for humans in any kind
of social interaction. Artificial systems, such as intelligent assistants,
would also greatly benefit from such mentalizing capabilities. However, humans
and systems alike are bound by limitations in their available computational
resources. This raises the need for satisficing mentalizing, reconciling
accuracy and efficiency in mental state inference that is good enough for a
given situation. In this paper, we present different Bayesian models of ToM
reasoning and evaluate them based on actual human behavior data that were
generated under different kinds of uncertainties. We propose a Switching
approach that combines specialized models, embodying simplifying presumptions,
in order to achieve a more statisficing mentalizing compared to a Full Bayesian
ToM model.
