Optimal Product Lifecycle and Partial Information with Active Learning
AbstractDuring production processes there is an accumulation of uncertainty aboutproduction and its output. However, decision-makers can reduce uncertainty bytaking measuremens. The role of value of measurements is discussed in adynamic production framework where the number of measurements depends on theattitude of a decision-maker toward risk, the unit cost of a measurement andits impact on the uncertainty, and the level of production and its quality.A model describing the way of handling such information in an active learningframework is shown by considering the impact of future uncertainty on currentdecisions. The model is illustrated using given functional forms and anumerical example. It is shown how the production level and its quality areaffected by the unit price of a measurement. Copyright Kluwer Academic Publishers 2003
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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 21 (2003)
Issue (Month): 1 (February)
production process; Kalman filtering; risk and uncertainty; value of information; dynamic programming; product lifecycle;
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