The theory of adaptive utility for sequential decision making offers a generalization of
the classical Bayesian approach, permitting initial utility uncertainty. This paper
examines how the possibility to learn preferences can be of interest for decisions in the
area of reliability. The resulting differences in determining optimal strategies are
explained and two examples are explored in which utility depends on the unknown cost of
system failure. The paper concludes with a commentary on further research required.
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