In this presentation I will discuss the incorporation of risk measures into multistage stochastic programs. Much attention has been recently devoted in the literature to this type of model, but there is no consensus on the best way to accomplish that goal. I will discuss pros and cons of the existing approaches, and propose a novel definition of consistency. A class of risk measures, which we call expected conditional risk measures, is discussed, and practical examples illustrate the use of this risk measure in realistic applications.
