Dynamic pricing and learning: historical origins, current research, and new directions

TLDR: In this paper, the authors provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning.

About: This article is published in Surveys in Operations Research and Management Science.The article was published on 2015-06-01 and is currently open access. It has received 245 citations till now. The article focuses on the topics: Dynamic pricing.

Frequently asked questions

Q1. What have the authors contributed in "Dynamic pricing and learning: historical origins, current research, and new directions" ?

The authors survey these literature streams: they provide a brief introduction to the historical origins of quantitative research on pricing and demand estimation, point to different subfields in the area of dynamic pricing, and provide an in-depth overview of the available literature on dynamic pricing and learning. The authors discuss relations with methodologically related research areas, and identify several important directions for future research. 

Q2. What is the focus of the paper?

The focus of the paper is on properties and numerical performance of an online-learning algorithm suitable for the complicated process considered by the authors. 

Q3. What is the goal of the literature on dynamic pricing and learning?

The common goal of the literature on dynamic pricing and learning is to develop pricing policies that take the intrinsic uncertainty about the relation between price and expected demand into account. 

Q4. What is the way to use a price-skimming strategy?

They show that if an infinite number of goods can be sold during a finite time interval, it is optimal to use a price-skimming strategy. 

Q5. What is the heuristic for a seller of a single item?

Mason and Välimäki (2011) consider a seller of a single item in an infinite time horizon, with maximizing the expected discounted reward as objective criterion. 

Q6. How does the authors prove a performance bound on decay balancing?

In addition they prove a performance bound on decay balancing, showing that the resulting expected discounted revenue is always at least one third of the optimal value. 

Q7. What is the definition of the demand function in the static monopoly pricing problem?

In the static monopoly pricing problem considered by Cournot (1838), the demand function is deterministic and completely known to the firm. 

Q8. How do they bridge the gap between robust and data-driven approaches to dynamic pricing?

Lobel and Perakis (2011) attempt to bridge the gap between robust and data-driven approaches to dynamic pricing, by considering a setting where the uncertainty set is deduced from data samples. 

Q9. How can an optimal price strategy be calculated?

In theory an optimal price strategy can be calculated by dynamic programming, but in practice this is computationally intractable. 

Q10. What is the way to show that a certainty equivalent pricing policy is not strongly consistent?

They show that a certainty equivalent pricing policy is not strongly consistent, by showing in an example that the limit of the price sequence is with positive probability different from the optimal price.