Dynamic Pricing: A learning Approach
Name
OR-355-01.pdf
Size
1.45 MB
Format
Adobe PDF
Checksum (MD5)
1abc65145b887ce9de9fc3de681ed76b
Author(s)
Bertsimas, Dimitris J.
Perakis, Georgia
Date Issued
August 2001
Publisher
Massachusetts Institute of Technology, Operations Research Center
Series/Report no.
Operations Research Center Working Paper;OR 355-01
Abstract
We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, but rather assume parametric families of demand functions that are learned over time. We first consider the noncompetitive case and present dynamic programming algorithms of increasing computational intensity with incomplete state information for jointly estimating the demand and setting prices as time evolves. Our computational results suggest that dynamic programming based methods outperform myopic policies often significantly. We then extend our analysis in a competitive environment with two firms. We introduce a more sophisticated model of demand learning, in which the price elasticities are slowly varying functions of time, and allows for increased flexibility in the modeling of the demand. We propose methods based on optimization for jointly estimating the Firm's own demand, its competitor's demand, and setting prices. In preliminary computational work, we found that optimization based pricing methods offer increased expected revenue for a firm independently of the policy the competitor firm is following.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
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