Journal
MANAGEMENT SCIENCE
Volume 61, Issue 4, Pages 723-739Publisher
INFORMS
DOI: 10.1287/mnsc.2014.2031
Keywords
model misspecification; inference; price optimization; revenue management; myopic pricing
Funding
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0964170] Funding Source: National Science Foundation
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We consider a multiperiod single product pricing problem with an unknown demand curve. The seller's objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon; in doing so, the seller needs to resolve the tension between learning the unknown demand curve and maximizing earned revenues. The main question that we investigate is the following: How large of a revenue loss is incurred if the seller uses a simple parametric model that differs significantly (i.e., is misspecified) relative to the underlying demand curve? We measure performance by analyzing the price trajectory induced by this misspecified model and quantifying the magnitude of revenue losses (as a function of the time horizon) relative to an oracle that knows the true underlying demand curve. The price of misspecification is expected to be significant if the parametric model is overly restrictive. Somewhat surprisingly, we show (under reasonably general conditions) that this need not be the case.
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