ABSTRACT: Ambiguous chance constrained optimization program (ACCP)
is a novel approach for producing ``robust'' decisions under data
uncertainty. In ambiguous chance constrained programs the
distributions of the random parameters in the problem are themselves
 uncertain. We assume that the uncertainty set of the distributions
is  a ``ball'' centered around a measure Q. The ACCP is
approximated by a robust sampled problem where each sample is drawn
according to the central measure Q. Our main contribution is to show
that the robust sampled problem is a good approximation for the
ambiguous chance constrained problem with a high probability and it
can be solved efficiently, both in
theory and in practice. We will also discuss an extension of this
theory to the two-period setting. This is a joint work with Garud
Iyengar.