Risk-Averse Control of Undiscounted Transient Markov Models
  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. 
Ozlem Cavus, Andrzej Ruszczynski
math.OC