  We consider risk-sensitive Markov decision processes (MDPs), where the MDP
model is influenced by a parameter which takes values in a compact metric
space. We identify sufficient conditions under which small perturbations in the
model parameters lead to small changes in the optimal value function and
optimal policy. We further establish the robustness of the risk-sensitive
optimal policies to modeling errors. Implications of the results for
data-driven decision-making, decision-making with preference uncertainty, and
systems with changing noise distributions are discussed.
