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http://ssrn.com/abstract=1983274
 
 

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On the (Surprising) Sufficiency of Linear Models for Dynamic Pricing with Demand Learning


Omar Besbes


Columbia Business School - Decision Risk and Operations

Assaf Zeevi


Columbia Business School - Decision Risk and Operations

March 2014

Columbia Business School Research Paper No. 12-5

Abstract:     
We consider a multi-period 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 which differs significantly (i.e., is misspecified) relative to the underlying demand curve. This "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 may not be the case.

Number of Pages in PDF File: 34

Keywords: model misspecification, inference, price optimization, revenue management, myopic pricing

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Date posted: January 12, 2012 ; Last revised: March 11, 2014

Suggested Citation

Besbes, Omar and Zeevi, Assaf, On the (Surprising) Sufficiency of Linear Models for Dynamic Pricing with Demand Learning (March 2014). Columbia Business School Research Paper No. 12-5. Available at SSRN: http://ssrn.com/abstract=1983274 or http://dx.doi.org/10.2139/ssrn.1983274

Contact Information

Omar Besbes (Contact Author)
Columbia Business School - Decision Risk and Operations ( email )
New York, NY
United States

Assaf Zeevi
Columbia Business School - Decision Risk and Operations ( email )
New York, NY
United States
212-854-9678 (Phone)
212-316-9180 (Fax)
HOME PAGE: http://www.gsb.columbia.edu/faculty/azeevi/

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