In this talk, we will formalize transparency as acting in a dynamical system or MDP in which we augment the physical state with the human's belief about the robot. We will characterize the dynamics model in this MDP, and show that approximate solutions lead to cars that drive in a way that is easier to anticipate, robots that come up with instructive demonstrations of their task knowledge, manipulator arms that clarify their intent, and navigation robots that clarify their future task plans. Lastly, we will briefly explore robots that express more interesting properties like the their level of confidence in their task, or the weight of an object they are carrying.