TY  - JOUR
A1  - Schlosser, Rainer
T1  - Heuristic mean-variance optimization in Markov decision processes using state-dependent risk aversion
JF  - IMA journal of management mathematics / Institute of Mathematics and Its Applications
N2  - In dynamic decision problems, it is challenging to find the right balance between maximizing expected rewards and minimizing risks. In this paper, we consider NP-hard mean-variance (MV) optimization problems in Markov decision processes with a finite time horizon. We present a heuristic approach to solve MV problems, which is based on state-dependent risk aversion and efficient dynamic programming techniques. Our approach can also be applied to mean-semivariance (MSV) problems, which particularly focus on the downside risk. We demonstrate the applicability and the effectiveness of our heuristic for dynamic pricing applications. Using reproducible examples, we show that our approach outperforms existing state-of-the-art benchmark models for MV and MSV problems while also providing competitive runtimes. Further, compared to models based on constant risk levels, we find that state-dependent risk aversion allows to more effectively intervene in case sales processes deviate from their planned paths. Our concepts are domain independent, easy to implement and of low computational complexity.
KW  - risk aversion
KW  - mean-variance optimization
KW  - Markov decision process;
KW  - dynamic programming
KW  - dynamic pricing
KW  - heuristics
Y1  - 2021
U6  - https://doi.org/10.1093/imaman/dpab009
SN  - 1471-678X
SN  - 1471-6798
VL  - 33
IS  - 2
SP  - 181
EP  - 199
PB  - Oxford Univ. Press
CY  - Oxford
ER  -