Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes
| Authors | |
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| Year of publication | 2018 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS '18) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1145/3209108.3209176 |
| Keywords | conditional value-at-risk; Markov chains; Markov decision processes; reachability; mean-payoff |
| Description | We present the conditional value-at-risk (CVaR) in the context of Markov chains and Markov decision processes with reachability and mean-payoff objectives. CVaR quantifies risk by means of the expectation of the worst p-quantile. As such it can be used to design risk-averse systems. We consider not only CVaR constraints, but also introduce their conjunction with expectation constraints and quantile constraints (value-at-risk, VaR). We derive lower and upper bounds on the computational complexity of the respective decision problems and characterize the structure of the strategies in terms of memory and randomization. |
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