Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/314977 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Mathematical Methods of Operations Research [ISSN:] 1432-5217 [Volume:] 99 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 141-178
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Markov decision process
Risk-sensitive decision
Optimized certainty equivalent
Optimal policy
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