{"title":"Dynamic Risk Measures for Finite-State Partially Observable Markov Decision Problems","authors":"Jingnan Fan, A. Ruszczynski","doi":"10.1137/1.9781611974072.22","DOIUrl":null,"url":null,"abstract":"In this paper, we provide a theory of time-consistent dynamic risk measures for finite-state partially observable Markov decision problems. By employing our new concept of stochastic conditional time consistency, we show that such dynamic risk measures have a special structure, given by transition risk mappings as risk measures on the space of functionals on the observable state space only. Moreover, these mappings enjoy a strong monotonicity with respect to first order stochastic dominance.","PeriodicalId":193106,"journal":{"name":"SIAM Conf. on Control and its Applications","volume":"453 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Conf. on Control and its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1137/1.9781611974072.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this paper, we provide a theory of time-consistent dynamic risk measures for finite-state partially observable Markov decision problems. By employing our new concept of stochastic conditional time consistency, we show that such dynamic risk measures have a special structure, given by transition risk mappings as risk measures on the space of functionals on the observable state space only. Moreover, these mappings enjoy a strong monotonicity with respect to first order stochastic dominance.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
