Original title: Risk-sensitive and Mean Variance Optimality in Continuous-time Markov Decision Chains
Authors: Sladký, Karel
Document type: Papers
Conference/Event: 36th International Conference Mathematical Methods in Economics, Jindřichův Hradec (CZ), 20180912
Year: 2018
Language: eng
Abstract: 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.
Keywords: certainty equivalent; connections between risk-sensitive and risk-neutral optimality; continuous-time Markov decision chains; exponential utility functions; mean-variance optimality
Project no.: GA18-02739S (CEP)
Funding provider: GA ČR
Host item entry: 36th International Conference Mathematical Methods in Economics, ISBN 978-80-7378-371-6

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2018/E/sladky-0493556.pdf
Original record: http://hdl.handle.net/11104/0286979

Permalink: http://www.nusl.cz/ntk/nusl-386563

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 Record created 2018-10-02, last modified 2019-10-20

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