Adaptive systems manage and regulate the behavior of devices or other systems using control loops to automatically adjust the value of some measured variables to equal the value of a desired set-point. These systems normally interact with physical parts or operate in physical environments, where uncertainty is unavoidable. Traditional approaches to manage that uncertainty use either robust control algorithms that consider bounded variations of the uncertain variables and worst-case scenarios, or adaptive control methods that estimate the parameters and change the control laws accordingly. In this paper we propose to include the sources of uncertainty in the system models as first-class entities using random variables, in order to simulate adaptive and control systems more faithfully, including not only the use of random variables to represent and operate with uncertain values, but also to represent decisions based on their comparisons. Two exemplar systems are used to illustrate and validate our proposal.