Revenue management with side information
Abstract/Contents
- Abstract
- Companies launch new products periodically without accurate prior knowledge of the true demand. One method to learn the demand function is price experimentation, where companies adaptively modify prices to learn the hidden demand function and use that to estimate the revenue-maximizing price. Nevertheless, proper price experimentation is a challenging problem due to the potential revenue loss during the learning horizon that can be substantially large. In fact, optimally balancing the trade-off between randomly selecting prices to expedite the learning versus selecting prices that maximize the expected earning has been subject of recent research in the operations management literature. A generic problem is to consider that a firm sells products over multiple periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. We establish a model to illustrate that this problem is closely related with bandits problem, which is a specific case of reinforcement learning. In addition, with accumulated data assisting to predict the true demand function, the price experimentation policy has some surprising feature. We demonstrate that, under certain conditions in the rich data environment, the challenging price experimentation problem could be solved by the ubiquitously used greedy pricing policy.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Qiang, Sheng |
|---|---|
| Associated with | Stanford University, Graduate School of Business. |
| Primary advisor | Bayati, Mohsen |
| Thesis advisor | Bayati, Mohsen |
| Thesis advisor | Harrison, J. Michael, 1944- |
| Thesis advisor | Xu, Kuang |
| Advisor | Harrison, J. Michael, 1944- |
| Advisor | Xu, Kuang |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sheng Qiang. |
|---|---|
| Note | Submitted to the Graduate School of Business. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Sheng Qiang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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