0410870 20240103182245.120060210235959.9 Edinburgh UK Operational Research Society 2002 1 s. 28 stochastic dynamic programming Markov decision chains mean-variance 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 GA402/01/0539 GA ČR cav_un_auth*0008959 CEZ:AV0Z1075907 We investigate how the minimum variance criterion can work in discrete stochastic dynamic programming. We adapt notions and notation used in Markov decision chains and in contrast to the classical models we also consider variance of the obtained total reward. Alternative definitions of the reward variance along with their mutual connections are discussed. Attention is also focused on finding policies minimizing the average reward variance on condition that the average reward is not less than a given value. cav_un_auth*0212941 IFORS 2002 Edinburgh GB 08.07.2002-12.07.2002 BB E http://hdl.handle.net/11104/0130957 UTIA-B 20020084 2002 2002 Edinburgh UK Operational Research Society International Federation of Operational Research Societies 2002. IFORS 2002. Abstracts 28