Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters

Chen, Qi; Jasin, Stefanus; Duenyas, Izak

doi:10.1287/opre.2020.2078

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…Recently, a large number of studies have appeared on dynamic pricing with demand learning (Besbes & Zeevi, 2009, 2015Broder & Rusmevichientong, 2012;Chen et al, 2019Chen et al, , 2021Cheung et al, 2017;den Boer & Zwart, 2014, 2015Hansen et al, 2021;Harrison et al, 2012;Keskin & Zeevi, 2014;Wang et al, 2014Wang et al, , 2021. We refer to den Boer (2015) for a recent survey.…”

Section: Related Literaturementioning

confidence: 99%

Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand

Loots

Boer

2023

Production and Operations Management

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We consider dynamic pricing and demand learning in a duopoly with multinomial logit demand, both from the perspective where firms compete against each other and from the perspective where firms aim to collude to increase revenues. We show that joint-revenue maximization is not always beneficial to both firms compared to the Nash equilibrium, and show that several other axiomatic notions of collusion can be constructed that are always beneficial to both firms and a threat to consumer welfare. Next, we construct a price algorithm and prove that it learns to charge supra-competitive prices if deployed by both firms, and learns to respond optimally against a class of competitive algorithms. Our algorithm includes a mechanism to infer demand observations from the competitor's price path, so that our algorithm can operate in a setting where prices are public but demand is private information. Our work contributes to the understanding of well-performing price policies in a competitive multi-agent setting, and shows that collusion by algorithms is possible and deserves the attention of lawmakers and competition policy regulators.

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Section: Related Literaturementioning

confidence: 99%

Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand

Loots

Boer

2023

Production and Operations Management

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“…For example, the linear model of I products requires

k\ge I+1

exploration prices, as discussed in Chen et al. (2021). They also mention that different choices of the exploration prices affect the empirical convergence rate in estimation, despite the same asymptotic rate.…”

Section: Modelmentioning

confidence: 99%

“…Nevertheless, the exploration prices need to be identified in advance, which may be Production and Operations Management impractical since the number k can be large. For example, the linear model of I products requires k ≥ I + 1 exploration prices, as discussed in Chen et al (2021). They also mention that different choices of the exploration prices affect the empirical convergence rate in estimation, despite the same asymptotic rate.…”

Section: Problem Of Incomplete Learningmentioning

confidence: 99%

Bayesian dithering for learning: Asymptotically optimal policies in dynamic pricing

Huh

Kim

Lin

2022

Production and Operations Management

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We consider a dynamic pricing and learning problem where a seller prices multiple products and learns from sales data about unknown demand. We study the parametric demand model in a Bayesian setting. To avoid the classical problem of incomplete learning, we propose dithering policies under which prices are probabilistically selected in a neighborhood surrounding the myopic optimal price. By analyzing the effect of dithering in facilitating learning, we establish regret upper bounds for three typical settings of demand model. We show that the dithering policy achieves an upper bound of order log T when the parameter set is finite. It can be modified to achieve a constant regret bound under an additional assumption. We also prove an upper bound of order √ T log T when the parameter set is compact and convex. Each bound matches (up to a logarithmic factor) the existing lower bound of any pricing policy. In this way, we show that dithering policies achieve asymptotically optimal performance in three different parameter settings, which demonstrates dithering as a unified approach to strike the balance between exploration and exploitation.

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Learning and Pricing with Inventory Constraints

Chen

Wang²,

Wang³

2022

Springer Series in Supply Chain Management

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Technical Note—Joint Learning and Optimization of Multi-Product Pricing with Finite Resource Capacity and Unknown Demand Parameters

Abstract: Dynamic Pricing with Limited Resource and Unknown Demand Parameters

Cited by 13 publications

References 21 publications

Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand

Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand

Bayesian dithering for learning: Asymptotically optimal policies in dynamic pricing

Learning and Pricing with Inventory Constraints

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