References (30)



An Entropy Based Methodology for Valuation of Demand Uncertainty Reduction

Adam Fleischhacker

University of Delaware

Pak-Wing Fok

University of Delaware - Department of Mathematics

April 24, 2014

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 requires neither sampled observations of demand nor a priori assumptions regarding the underlying demand distribution. Rather, leveraging the maximum entropy principle to assign a probability density over all possible demand distributions enables modeling of both one's present state of uncertainty and one's potential future states of uncertainty. We demonstrate that this probability assignment is intuitively satisfying, theoretically justified, and done in a manner that is completely consistent with a decision maker's current information (or lack thereof). Theoretical and numerical results for valuing uncertainty reductions without knowing an underlying demand distribution are explored and contribute to the existing distribution free literature. We leverage these results to answer an often overlooked question in demand management: "Is there value in further reducing my demand uncertainty or do I act on my currently available information?''

Number of Pages in PDF File: 39

Keywords: Maximum Entropy Principle, Expected Value of Information, Distribution Free Demand Models

Open PDF in Browser Download This Paper

Date posted: April 26, 2014  

Suggested Citation

Fleischhacker, Adam and Fok, Pak-Wing, An Entropy Based Methodology for Valuation of Demand Uncertainty Reduction (April 24, 2014). Available at SSRN: http://ssrn.com/abstract=2428870 or http://dx.doi.org/10.2139/ssrn.2428870

Contact Information

Adam Fleischhacker (Contact Author)
University of Delaware ( email )
Newark, DE
United States
Pak-Wing Fok
University of Delaware - Department of Mathematics ( email )
United States
Feedback to SSRN

Paper statistics
Abstract Views: 367
Downloads: 68
Download Rank: 251,521
References:  30

© 2016 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollobot1 in 0.234 seconds