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

34 Pages Posted: 12 Jan 2012 Last revised: 11 Mar 2014

Omar Besbes

Columbia Business School - Decision Risk and Operations

Assaf Zeevi

Columbia Business School - Decision Risk and Operations

Date Written: March 2014

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.

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

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: https://ssrn.com/abstract=1983274 or http://dx.doi.org/10.2139/ssrn.1983274

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/

Paper statistics

Downloads
609
Rank
34,696
Abstract Views
1,845