There are many naturally multi-stage (i.e., more than two) stochastic programming problems that are approximated by a two-stage stochastic programming model due to the complete intractability of the 'real' model. That is, we act as if all uncertainty regarding all future states is revealed after the first stage decision. Then this approximation can be used in a rolling horizon fashion to guide dynamic decision support. I think (am I correct?) this is a generally accepted method to approach such problems.

Are there any tips and tricks to do so? For example, it feels intuitive to add more weight to nearby decisions than to decisions that are still far away:)

There are many naturally multi-stage (i.e., more than two) stochastic programming problems that are approximated by a two-stage stochastic programming model due to the complete intractability of the 'real' model. For example, consider a 10 period location-transportation problem with some dependencies between the periods so that the solution of one of the periods determines possible recourse actions for the period thereafter. With a two-stage approximation I imply that we consider, in terms of location-transportation, a first-stage decision where we determine the locations to be opened in a first-stage decision. Then, we act as if all information of the 10 periods is presented to the decision-maker, and we make transportation plans for the upcoming 10 periods as a single second-stage decision. The 'real' model here would be a 10-stage stochastic program, but is intractable for many applications (the location-transportation might actually be a problem that is 'relatively' easy, so don't take it to literally)

So, we act as if all uncertainty regarding all future states is revealed after the first stage decision, and this first-stage decision is taken for all future periods directly. Then this approximation can be used in a rolling horizon fashion to guide dynamic decision support. I think (am I correct?) this is a generally accepted method to approach such problems.

Are there any tips and tricks to do so? For example, it feels intuitive to add more weight to nearby decisions than to decisions that are still far away:)

There are many naturally multi (more than two) stage stochastic programming problems that are approximated by a two stage stochastic programming model due to the complete intractability of the 'real' model. That is, we act as if all uncertainty regarding all future states is revealed after the first stage decision. Then this approximation can be used in a rolling horizon fashion to guide dynamic decision support. I think (am I correct?) this is a generally accepted method to approach such problems.

Are there any tips and tricks to do so? For example, it feels intuitive to add more weight to nearby decisions than to decisions that are still far away:)

There are many naturally multi-stage (i.e., more than two) stochastic programming problems that are approximated by a two-stage stochastic programming model due to the complete intractability of the 'real' model. That is, we act as if all uncertainty regarding all future states is revealed after the first stage decision. Then this approximation can be used in a rolling horizon fashion to guide dynamic decision support. I think (am I correct?) this is a generally accepted method to approach such problems.

Are there any tips and tricks to do so? For example, it feels intuitive to add more weight to nearby decisions than to decisions that are still far away:)