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

Satisficing Mentalizing: Bayesian Models of Theory of Mind Reasoning in Scenarios with Different Uncertainties