  Multistage stochastic optimization problems are, by essence, complex as their
solutions are indexed both by stages and by uncertainties. Their large scale
nature makes decomposition methods appealing, like dynamic programming which is
a sequential decomposition using a state variable defined at all stages. In
this paper, we introduce the notion of state reduction by time blocks, that is,
at stages that are not necessarily all the original stages. Then, we prove a
reduced dynamic programming equation. We position our result with respect to
the most well-known mathematical frameworks for dynamic programming. We
illustrate our contribution by showing its potential for applied problems with
two time scales.
