A fundamental difficulty in stochastic optimization is the fact that
decisions may not be able pin down the values of future costs, but
rather can only, within limits, shape their distributions as random variables.
An upper bound on a ramdom cost is often impossible, or too expensive, to
enforce with certainty, and so some compromise attitude must be taken to
the violations that might occur. Similarly, there is no instant
interpretation of what it might mean to minimize a random cost, apart