  We present a comparative study of two methods for the reduction of the
dimensionality of a system of ordinary differential equations that exhibits
time-scale separation. Both methods lead to a reduced system of stochastic
differential equations. The novel feature of these methods is that they allow
the use, in the reduced system, of higher order terms in the resolved
variables. The first method, proposed by Majda, Timofeyev and Vanden-Eijnden,
is based on an asymptotic strategy developed by Kurtz. The second method is a
short-memory approximation of the Mori-Zwanzig projection formalism of
irreversible statistical mechanics, as proposed by Chorin, Hald and Kupferman.
We present conditions under which the reduced models arising from the two
methods should have similar predictive ability. We apply the two methods to
test cases that satisfy these conditions. The form of the reduced models and
the numerical simulations show that the two methods have similar predictive
ability as expected.
