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A pricing problem with unknown arrival rate and price sensitivity

A pricing problem with unknown arrival rate and price sensitivity
A pricing problem with unknown arrival rate and price sensitivity

We study a pricing problem with finite inventory and semi-parametric demand uncertainty. Demand is a price-dependent Poisson process whose mean is the product of buyers’ arrival rate, which is a constant λ, and buyers’ purchase probability q(p) , where p is the price. The seller observes arrivals and sales, and knows neither λ nor q. Based on a non-parametric maximum-likelihood estimator of (λ, q) , we construct an estimator of mean demand and show that as the system size and number of prices grow, it is asymptotically more efficient than the maximum likelihood estimator based only on sale data. Based on this estimator, we develop a pricing algorithm paralleling (Besbes and Zeevi in Oper Res 57:1407–1420, 2009) and study its performance in an asymptotic regime similar to theirs: the initial inventory and the arrival rate grow proportionally to a scale parameter n. If q and its inverse function are Lipschitz continuous, then the worst-case regret is shown to be O((log n/ n) 1 / 4). A second model considered is the one in Besbes and Zeevi (2009, Section 4.2), where no arrivals are involved; we modify their algorithm and improve the worst-case regret to O((log n/ n) 1 / 4). In each setting, the regret order is the best known, and is obtained by refining their proof methods. We also prove an Ω (n - 1 / 2) lower bound on the regret. Numerical comparisons to state-of-the-art alternatives indicate the effectiveness of our arrivals-based approach.

Asymptotic analysis, Asymptotic efficiency, Estimation, Exploration–exploitation, Regret
1432-2994
77-106
Avramidis, Athanasios
d6c4b6b6-c0cf-4ed1-bbe1-a539937e4001
Avramidis, Athanasios
d6c4b6b6-c0cf-4ed1-bbe1-a539937e4001

Avramidis, Athanasios (2020) A pricing problem with unknown arrival rate and price sensitivity. Mathematical Methods of Operations Research, 92 (1), 77-106. (doi:10.1007/s00186-020-00704-y).

Record type: Article

Abstract

We study a pricing problem with finite inventory and semi-parametric demand uncertainty. Demand is a price-dependent Poisson process whose mean is the product of buyers’ arrival rate, which is a constant λ, and buyers’ purchase probability q(p) , where p is the price. The seller observes arrivals and sales, and knows neither λ nor q. Based on a non-parametric maximum-likelihood estimator of (λ, q) , we construct an estimator of mean demand and show that as the system size and number of prices grow, it is asymptotically more efficient than the maximum likelihood estimator based only on sale data. Based on this estimator, we develop a pricing algorithm paralleling (Besbes and Zeevi in Oper Res 57:1407–1420, 2009) and study its performance in an asymptotic regime similar to theirs: the initial inventory and the arrival rate grow proportionally to a scale parameter n. If q and its inverse function are Lipschitz continuous, then the worst-case regret is shown to be O((log n/ n) 1 / 4). A second model considered is the one in Besbes and Zeevi (2009, Section 4.2), where no arrivals are involved; we modify their algorithm and improve the worst-case regret to O((log n/ n) 1 / 4). In each setting, the regret order is the best known, and is obtained by refining their proof methods. We also prove an Ω (n - 1 / 2) lower bound on the regret. Numerical comparisons to state-of-the-art alternatives indicate the effectiveness of our arrivals-based approach.

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More information

Submitted date: 11 February 2019
Accepted/In Press date: 24 January 2020
e-pub ahead of print date: 11 February 2020
Published date: 1 August 2020
Additional Information: Publisher Copyright: © 2020, The Author(s).
Keywords: Asymptotic analysis, Asymptotic efficiency, Estimation, Exploration–exploitation, Regret

Identifiers

Local EPrints ID: 425609
URI: http://eprints.soton.ac.uk/id/eprint/425609
ISSN: 1432-2994
PURE UUID: 19b2149d-76e0-439b-988e-57b23897b519
ORCID for Athanasios Avramidis: ORCID iD orcid.org/0000-0001-9310-8894

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Date deposited: 25 Oct 2018 16:30
Last modified: 21 Jun 2022 01:39

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