  This paper introduces a node formulation for multistage stochastic programs
with endogenous (i.e., decision-dependent) uncertainty. Problems with such
structure arise when the choices of the decision maker determine a change in
the likelihood of future random events. The node formulation avoids an explicit
statement of non-anticipativity constraints, and as such keeps the dimension of
the model sizeable. An exact solution algorithm for a special case is
introduced and tested on a case study. Results show that the algorithm
outperforms a commercial solver as the size of the instances increases.
