Entropic risk for turn-based stochastic games
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Entropic risk (ERisk) is an established risk measure in finance, quantifying risk by an exponential re-weighting of rewards. We study ERisk for the first time in the context of turn-based stochastic games with the total reward objective. This gives rise to an objective function that demands the control of systems in a risk-averse manner. We show that the resulting games are determined and, in particular, admit optimal memoryless deterministic strategies. This contrasts risk measures that previously have been considered in the special case of Markov decision processes and that require randomization and/or memory. We provide several results on the decidability and the computational complexity of the threshold problem, i.e. whether the optimal value of ERisk exceeds a given threshold. Furthermore, an approximation algorithm for the optimal value of ERisk is provided.
Details
Original language | English |
---|---|
Article number | 105214 |
Journal | Information and computation |
Volume | 301(2024) |
Issue number | Part A |
Early online date | Aug 2024 |
Publication status | Published - Dec 2024 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0002-5321-9343/work/167215174 |
---|---|
ORCID | /0000-0003-4829-0476/work/167216986 |