Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314977 
Year of Publication: 
2024
Citation: 
[Journal:] Mathematical Methods of Operations Research [ISSN:] 1432-5217 [Volume:] 99 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 141-178
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
The paper provides an overview of the theory and applications of risk-sensitive Markov decision processes. The term ’risk-sensitive’ refers here to the use of the Optimized Certainty Equivalent as a means to measure expectation and risk. This comprises the well-known entropic risk measure and Conditional Value-at-Risk. We restrict our considerations to stationary problems with an infinite time horizon. Conditions are given under which optimal policies exist and solution procedures are explained. We present both the theory when the Optimized Certainty Equivalent is applied recursively as well as the case where it is applied to the cumulated reward. Discounted as well as non-discounted models are reviewed.
Subjects: 
Markov decision process
Risk-sensitive decision
Optimized certainty equivalent
Optimal policy
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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