euclid.jap.1261670699

# In fact direct calculation shows that var j2 e 1 e

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Unformatted text preview: ess receives a lump sum reward of 1. Let the time interval between jumps be 1 unit of time. The discount factor is α and T ∼ exp(α). The total discounted rewards under the two deﬁnitions are respectively ∞ J1 = n=1 e−αn = e−α , 1 − e−α N(T ) J2 = 1 = N (T ). n=1 Note that J1 is a deterministic number and J2 is a random variable depending on T . Thus, var (J1 ) = 0 < var (J2 ). In fact, direct calculation shows that var (J2 ) = e−α (1 − e−α )2 . Acknowledgement This research was supported by NSF grants CMMI-0600538 and CMMI-0928490. References [1] Baykal-Gürsoy, M. and Gürsoy, K. (2007). Semi-Markov decision processes: nonstandard criteria. Prob. Eng. Inf. Sci. 21, 635–657. [2] Feinberg, E. A. (2004). Continuous time discounted jump Markov decision processes: a discrete-event approach. Math. Operat. Res. 29, 492–524. [3] Fristedt, B. and Gray, L. (1997). A Modern Approach to Probability Theory. Birkhäuser, Boston, MA. [4] Jaquette, S. C. (1975). Markov decision processes with a new optimality criterion: continuous time. Ann. Statist. 3, 547–553. [5] Markowitz, H. M. (1952). Portfolio selection. J. Finance 7, 77–91. [6] Shiryaev, A. N. (1996). Probability, 2nd edn. Springer, New York. [7] Sobel, M. J. (1982). The variance of discounted Markov decision processes. J. Appl. Prob. 19, 794–802. [8] Sobel, M. J. (1985). Maximal mean/standard deviation ratio in an undiscounted MDP. Operat. Res. Lett. 4, 157–159. [9] Sobel, M. J. (1994). Mean-variance tradeoffs in an undiscounted MDP. Operat. Res. 42, 175–183. [10] Van Dijk, N. M. and Sladký, K. (2006). On the total reward variance for continuous-time Markov reward chains. J. Appl. Prob. 43, 1044–1052. [11] White, D. J. (1988). Mean, variance, and probabilistic criteria in ﬁnite Markov decision processes: a review. J. Optimization Theory Appl. 56, 1–29....
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