A Revenue Maximizing Strategy Based on Bayesian Analysis of Demand Dynamics

Li, Jiahan; Yao, Tao; Gao, Haibing

doi:10.1137/1.9781611973303.20

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Bertsimas and Perakis (2006) consider least-squares learning in an oligopoly with finite inventories and linear demand function, and propose an algorithm for estimation and pricing. Kwon et al (2009), Li et al (2010), Chung et al (2012) adopt the framework of differential variational inequalities to study a capacitated oligopoly, propose an algorithm to solve these equations, and estimate unknown parameters via Kalman filtering. Perakis and Sood (2006) (see also ) take a robust-optimization approach to the dynamic oligopoly pricing problem, and study Nash equilibrium policies.…”

Section: Literaturementioning

confidence: 99%

Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

Geer

Boer

Bayliss

et al. 2018

J Revenue Pricing Manag

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This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17 th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.

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Section: Literaturementioning

confidence: 99%

Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

Geer

Boer

Bayliss

et al. 2018

J Revenue Pricing Manag

View full text Add to dashboard Cite

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Demand Estimation in Airline Revenue Management

Bartke¹

2014

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Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions

Boer

2013

SSRN Journal

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Dynamic pricing and learning is a research topic that has received a considerable amount of attention in recent years, from different scientific communities: operations research and management science, marketing, economics, econometrics, and computer science. We survey these literature streams: we 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. We discuss relations with methodologically related research areas, and identify several important directions for future research.

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A Revenue Maximizing Strategy Based on Bayesian Analysis of Demand Dynamics

Cited by 3 publications

References 18 publications

Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

Demand Estimation in Airline Revenue Management

Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions

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