Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms

By Omar Besbes, Assaf J. Zeevi. Operational Research, 2009

545 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b796a8ae8b2ef629c5e02204356a71b112c1b6a2

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In Computer Networks and Communications

In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization (2016, background): “In particular, this is the case in dynamic demand learning and pricing as in Bertsimas and Perakis (2006), Besbes and Zeevi (2009), Harrison et al. (2012) because each sale event is assumed independent and nothing about a present sale event is considered when setting the price.”
Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information (2015, methodology): “Besbes and Zeevi (2009) consider a problem with a ﬁnite initial inventory and a ﬁnite planning horizon where the demand function has a known form but with some unknown parameters.”
Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions (2013, unknown): “The proof makes use of information-theoretic inequalities and techniques found in Besbes and Zeevi (2011).”
Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies (2014, methodology): “Besbes and Zeevi (2011) and Broder and Rusmevichientong (2012) provide lower bounds on regret that grow proportional to √ T in similar dynamic pricing contexts with demand model uncertainty.”
Bandits with Knapsacks (2013, methodology): “The algorithm (7)It is worth noting that a closely related problem – dynamic pr icing with known Bayesian priors – has a rich literature in Operations Research, see [11] for an overview.”
Introduction to Multi-Armed Bandits (2019, methodology): “ regular demands, for the special case B> (T), has also appeared in a simultaneous and independent paper (Wang et al., 2014). The study of dynamic pricing with limited supply has been initiated in Besbes and Zeevi (2009). They considered the case of B> (T), and achieved regret O~(T3=4) against”
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms (2013, background): “[24] [4] [10] [33] extend this approach to handle complex (possibly infinite) action spaces.”
On Kernelized Multi-armed Bandits (2017, background): “ cases. 1. Introduction Optimization over large domains under uncertainty is an important subproblem arising in a variety of sequential decision making problems, such as dynamic pricing in economics (Besbes and Zeevi, 2009), reinforcement learning with continuous state/action spaces (Kaelbling et al”