The purpose of this talk is to discuss the role of uncertainty
in optimization. If the world is indeed stochastic, can we model
it appropriately using deterministic models? Can we get a good
picture of the situation by asking many "what-if"-questions? That
is what our textbooks say. Using examples, I will show that such
methodology does not capture stochastics in an appropriate way.
In particular, all aspects of flexibility will be lost. Or in
a different language, we will overlook implicit options as well
as the fact that we may have killed other options. The talk is
meant as an encouragement for starting to look at stochastics
in combinatorial optimization.
