Stochastic programming with recourse usually assumes uncertainty to be exogenous. Decision-dependent probabilities in stochastic programs with recourse (click here for free access) presents modelling and application of decision-dependent uncertainty in mathematical programming including a taxonomy of stochastic programming recourse models with decision-dependent uncertainty. The work includes several ways of incorporating direct or indirect manipulation of underlying probability distributions through decision variables in two-stage stochastic programming problems. Two-stage models are formulated where prior probabilities are distorted through an affine transformation or combined using a convex combination of several probability distributions. Additionally, the paper presents models where the parameters of the probability distribution are first-stage decision variables. Test instances for each formulation are solved with a commercial solver, BARON, using selective branching.