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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains
 Sladký, Karel
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.
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Optimalita za rizika a typu střední hodnota - rozptyl v markovskýách rozhodovacích procesech
 Sladký, Karel ; Sitař, Milan
In this note, we compare two aproaches for handling risk-variability features arising in discrete-time Markov decision processes: models with exponential utility function and mean variance optimality models. Computational approaches for finding optimal decision with respect to the optimality criteria mentioned above are presented and analytical results showing connections between the above optimality criteria are discussed.
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