A partially observed markov decision process for dynamic pricing

Yossi Aviv*, Amit Pazgal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

124 Scopus citations


In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework to study a dynamic pricing problem faced by sellers of fashion-like goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings together various types of uncertainties about the demand, some of which are resolvable through sales observations. We develop a rigorous upper bound for the seller's optimal dynamic decision problem and use it to propose an active-learning heuristic pricing policy. We conduct a numerical study to test the performance of four different heuristic dynamic pricing policies in order to gain insight into several important managerial questions that arise in the context of revenue management.

Original languageEnglish
Pages (from-to)1400-1416
Number of pages17
JournalManagement Science
Issue number9
StatePublished - Sep 2005
Externally publishedYes


  • Learning
  • Partially observed Markov decision processes
  • Pricing
  • Revenue management


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