Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments-Reference-Cited by-同舟云学术

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Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments

Published:2019-03 Issue:2 Volume:38 Page:226-252
ISSN:0732-2399
Container-title:Marketing Science
language:en
Short-container-title:Marketing Science

Author:

Misra Kanishka¹^ORCID,Schwartz Eric M.²,Abernethy Jacob³^ORCID

Affiliation:

1. Rady School of Management, University of California, San Diego, La Jolla, California 92093;

2. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109;

3. School of Computer Science, College of Computing, Georgia Institute of Technologyy, Atlanta, Georgia 30332

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Marketing,Business and International Management

Reference65 articles.

1. Conditioning Prices on Purchase History

2. Optimal Learning by Experimentation

3. Sample mean based index policies by O(log n) regret for the multi-armed bandit problem

4. Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice

5. Price Stickiness: Empirical Evidence of the Menu Cost Channel

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