An Entropy-Based Methodology for Valuation of Demand Uncertainty Reduction

An Entropy-Based Methodology for Valuation of Demand Uncertainty Reduction

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Article ID: iaor201530496
Volume: 46
Issue: 6
Start Page Number: 1165
End Page Number: 1198
Publication Date: Dec 2015
Journal: Decision Sciences
Authors: ,
Keywords: information theory, demand, economics, marketing
Abstract:

We propose a distribution‐free entropy‐based methodology to calculate the expected value of an uncertainty reduction effort and present our results within the context of reducing demand uncertainty. In contrast to existing techniques, the methodology does not require a priori assumptions regarding the underlying demand distribution, does not require sampled observations to be the mechanism by which uncertainty is reduced, and provides an expectation of information value as opposed to an upper bound. In our methodology, a decision maker uses his existing knowledge combined with the maximum entropy principle to model both his present and potential future states of uncertainty as probability densities over all possible demand distributions. Modeling uncertainty in this way provides for a theoretically justified and intuitively satisfying method of valuing an uncertainty reduction effort without knowing the information to be revealed. We demonstrate the methodology's use in three different settings: (i) a newsvendor valuing knowledge of expected demand, (ii) a short life cycle product supply manager considering the adoption of a quick response strategy, and (iii) a revenue manager making a pricing decision with limited knowledge of the market potential for his product.

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