  Coherent lower previsions are general probabilistic models allowing
incompletely specified probability distributions. However, for complete
description of a coherent lower prevision -- even on finite underlying sample
spaces -- an infinite number of assessments is needed in general. Therefore,
they are often only described approximately by some less general models, such
as coherent lower probabilities or in terms of some other finite set of
constraints. The magnitude of error induced by the approximations has often
been neglected in the literature, despite the fact that it can be significant,
with substantial impact on consequent decisions. An apparent reason is that no
widely used general method for estimating the error seems to be available at
the moment.
  This paper provides a practically applicable method that allows calculating
an upper bound for the maximal error induced by approximating a coherent lower
probability with its values on a finite set of gambles. An algorithm is also
provided with an estimation of its computational complexity.
