  Recently it has been argued that entropy can be a direct measure of
complexity, where the smaller value of entropy indicates lower system
complexity, while its larger value indicates higher system complexity. We
dispute this view and propose a universal measure of complexity based on the
Gell-Mann's view of complexity. Our universal measure of complexity bases on a
non-linear transformation of time-dependent entropy, where the system state
with the highest complexity is the most distant from all the states of the
system of lesser or no complexity. We have shown that the most complex is
optimally mixed states consisting of pure states i.e., of the most regular and
most disordered which the space of states of a given system allows. A
parsimonious paradigmatic example of the simplest system with a small and a
large number of degrees of freedom, is shown to support this methodology.
Several important features of this universal measure are pointed out,
especially its flexibility (i.e., its openness to extensions), ability to the
analysis of a system critical behavior, and ability to study the dynamic
complexity.
