  Uncertainty is an inherent property of any complex system, especially those
that integrate physical parts or operate in real environments. In this paper,
we focus on the Digital Twins of adaptive systems, which are particularly
complex to design, verify, and optimize. One of the problems of having two
systems (the physical one and its digital replica) is that their behavior may
not always be consistent. In addition, both twins are normally subject to
different types of uncertainties, which complicates their comparison. In this
paper we propose the explicit representation and treatment of the uncertainty
of both twins, and show how this enables a more accurate comparison of their
behaviors. Furthermore, this allows us to reduce the overall system uncertainty
and improve its behavior by properly averaging the individual uncertainties of
the two twins. An exemplary incubator system is used to illustrate and validate
our proposal.
