  Intuitively, obedience -- following the order that a human gives -- seems
like a good property for a robot to have. But, we humans are not perfect and we
may give orders that are not best aligned to our preferences. We show that when
a human is not perfectly rational then a robot that tries to infer and act
according to the human's underlying preferences can always perform better than
a robot that simply follows the human's literal order. Thus, there is a
tradeoff between the obedience of a robot and the value it can attain for its
owner. We investigate how this tradeoff is impacted by the way the robot infers
the human's preferences, showing that some methods err more on the side of
obedience than others. We then analyze how performance degrades when the robot
has a misspecified model of the features that the human cares about or the
level of rationality of the human. Finally, we study how robots can start
detecting such model misspecification. Overall, our work suggests that there
might be a middle ground in which robots intelligently decide when to obey
human orders, but err on the side of obedience.
