  We use Markov risk measures to formulate a risk-averse version of the
undiscounted total cost problem for a transient controlled Markov process. We
derive risk-averse dynamic programming equations and we show that a randomized
policy may be strictly better than deterministic policies, when risk measures
are employed. We illustrate the results on an optimal stopping problem and an
organ transplant problem.
