  We analyze the effect of small changes in the underlying probabilistic model
on the value of multi-period stochastic optimization problems and optimal
stopping problems. We work in finite discrete time and measure these changes
with the adapted Wasserstein distance. We prove explicit first-order
approximations for both problems. Expected utility maximization is discussed as
a special case.
