The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
The Value of Knowing a Demand Curve: Bounds on Regret for Online Posted-Price Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Commissioned Paper: An Overview of Pricing Models for Revenue Management
Manufacturing & Service Operations Management
Prediction, Learning, and Games
Prediction, Learning, and Games
A Nonparametric Approach to Multiproduct Pricing
Operations Research
Relative Entropy, Exponential Utility, and Robust Dynamic Pricing
Operations Research
Toward Robust Revenue Management: Competitive Analysis of Online Booking
Operations Research
Dynamic Pricing for Nonperishable Products with Demand Learning
Operations Research
Robust Controls for Network Revenue Management
Manufacturing & Service Operations Management
INFOCOM'10 Proceedings of the 29th conference on Information communications
Pricing and Dimensioning Competing Large-Scale Service Providers
Manufacturing & Service Operations Management
On the Minimax Complexity of Pricing in a Changing Environment
Operations Research
Adaptive Strategies for Dynamic Pricing Agents
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Dynamic pricing with limited supply
Proceedings of the 13th ACM Conference on Electronic Commerce
Learning on a budget: posted price mechanisms for online procurement
Proceedings of the 13th ACM Conference on Electronic Commerce
Dynamic Pricing Under a General Parametric Choice Model
Operations Research
Dynamic Pricing with Financial Milestones: Feedback-Form Policies
Management Science
Blind Network Revenue Management
Operations Research
Blind Network Revenue Management
Operations Research
Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising
Operations Research
Bayesian Dynamic Pricing in Queueing Systems with Unknown Delay Cost Characteristics
Manufacturing & Service Operations Management
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms
Proceedings of the 22nd international conference on World Wide Web
Optimal Dynamic Assortment Planning with Demand Learning
Manufacturing & Service Operations Management
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We consider a single-product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve) is not known. We consider two instances of this problem: (i) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and (ii) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function “on the fly,” and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is “close” to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function, manifested as the revenue loss due to model uncertainty.