Optimal solution for undiscounted variance penalized Markov decision chains. Abstract

0410871 20240103182245.220060210235959.9 Optimal solution for undiscounted variance penalized Markov decision chains. Abstract Berlin HumboldtUniversity Berlin 2002 1 s. Mathematical Methods in Economy and Industry. Abstracts14 Markov decision chains optimal policies mean-variance penalization cav_un_auth*0101196 Sladký Karel UTIA-B Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. 12B GA402/02/1015 GA ČR cav_un_auth*0000527 GA402/02/0539 GA ČR cav_un_auth*0212942 CEZ:AV0Z1075907 We investigate how the mean variance selection rule can work in Markovian decision models. In contrast to the classical models we assume that instead of maximizing the mean reward per transition we consider more sophisticated criteria taking into account also higher moments and the variance of the cumulative reward. Properties of optimal policies as well as optimization procedures with respect to the above criteria are discussed primarily for undiscounted long run models. cav_un_auth*0212934 Joint Czech-German-Slovak Conference /12./ Arnstadt DE 22.07.2002-26.07.2002 BB E http://hdl.handle.net/11104/0130958 UTIA-B 20020085 2002 2002 Berlin HumboldtUniversity Berlin Mathematical Methods in Economy and Industry. Abstracts 14