  We develop a method for computing policies in Markov decision processes with
risk-sensitive measures subject to temporal logic constraints. Specifically, we
use a particular risk-sensitive measure from cumulative prospect theory, which
has been previously adopted in psychology and economics. The nonlinear
transformation of the probabilities and utility functions yields a nonlinear
programming problem, which makes computation of optimal policies typically
challenging. We show that this nonlinear weighting function can be accurately
approximated by the difference of two convex functions. This observation
enables efficient policy computation using convex-concave programming. We
demonstrate the effectiveness of the approach on several scenarios.
