TY - GEN TI - Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains T3 - 36th International Conference Mathematical Methods in Economics AU - Sladký, Karel AB - In this note we consider continuous-time Markov decision processes with finite state and actions spaces where the stream of rewards generated by the Markov processes is evaluated by an exponential utility function with a given risk sensitivitycoefficient (so-called risk-sensitive models). If the risk sensitivity coefficient equals zero (risk-neutral case) we arrive at a standard Markov decision process. Then we can easily obtain necessary and sufficient mean reward optimality conditions and the variability can be evaluated by the mean variance of total expected rewards. For the risk-sensitive case, i.e. if the risk-sensitivity coefficient is non-zero, for a given value of the risk-sensitivity coefficient we establish necessary and sufficient optimality conditions for maximal (or minimal) growth rate of expectation of the exponential utility function, along with mean value of the corresponding certainty equivalent. Recall that in this case along with the total reward also its higher moments are taken into account. SN - 978-80-7378-371-6 UR - http://hdl.handle.net/11104/0286979 UR - http://www.nusl.cz/ntk/nusl-386563 LA - eng KW - connections between risk-sensitive and risk-neutral optimality KW - continuous-time Markov decision chains KW - mean-variance optimality KW - certainty equivalent KW - exponential utility functions PY - 2018 PB - Ústav teorie informace a automatizace, Pod vodárenskou věží 4, 182 08 Praha 8, http://www.utia.cas.cz/ ER -