# Decision dependent scenarios in Stochastic Programming

I am new to the field of stochastic programming and saw that the general formulation of a two-stage stochastic programming problem is given by: $$\min_{x\in X}\{ g(x)= f(x) + E_{\xi}[Q(x,\xi)]\}$$

where $$\xi$$ is the realization of uncertain data. Techniques such as Benders' Decomposition can be applied to solve these problems. However, I did not come across examples where $$\xi$$ is dependent on our decision in the first stage $$x$$, i.e. we would deal with $$\xi(x)$$. Can the same solution methods be applied for such cases and if not, what are standard approaches to solve such decision dependent scenarios?

A hopefully more useful answer would be to study the specific problem at hand. Particularly, how does your decision $$x$$ influence $$\xi$$? Is your $$\xi$$ continuous or discrete? (probably the latter since you say scenarios). There are at least two ways in which $$x$$ can influence $$\xi$$. It can influence the timing when $$\xi$$ is revealed (probably not your case), see for example this article. Or it can influence the distribution function or support of your $$\xi$$ (probably your case, I guess). In the latter case, one can still distinguish a couple of cases:
• A. Where you can obtain infinitely many different distributions of $$\xi$$ as a result of $$x$$
• Thanks for the answer! Indeed, my $\xi$ is discrete and there is a finite amount of distributions, but still potentially exponentially many. Aug 2, 2022 at 11:24