Action-timing problem with sequential Bayesian belief revision process

We consider the problem of deciding the best action time when observations are made sequentially. Specifically we address a special type of optimal stopping problem where observations are made from state-contingent distributions and there exists uncertainty on the state. In this paper, the decision-maker's belief on state is revised sequentially based on the previous observations. By using the independence property of the observations from a given distribution, the sequential Bayesian belief revision process is represented as a simple recursive form. The methodology developed in this paper provides a new theoretical framework for addressing the uncertainty on state in the action-timing problem context. By conducting a simulation analysis, we demonstrate the value of applying Bayesian strategy which uses sequential belief revision process. In addition, we evaluate the value of perfect information to gain more insight on the effects of using Bayesian strategy in the problem.
Publisher
Elsevier
Issue Date
1998
Keywords

Decision theory; Stochastic dynamic programming; Bayesian analysis; Action-timing problem; Simulation

Citation

European Journal of Operational Research. Vol. 105, No. 1, pp. 118-129

ISSN
0377-2217
DOI
10.1016/S0377-2217(97)00036-2
URI
http://hdl.handle.net/10203/8857
Appears in Collection
KSIM-Journal Papers(저널논문)
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