Remarkable progress has been made in the development of algorithmic pr
ocedures and the availability of software for stochastic programming p
roblems. However, some fundamental questions have remained unexplored.
This paper identifies the more challenging open questions in the fiel
d of stochastic programming. Some are purely technical in nature, but
many also go to the foundations of designing models for decision makin
g under uncertainty.