Dynamic Pricing with Adversarially-Censored Demands

• URL: http://arxiv.org/abs/2502.06168v1
• Date: Mon, 10 Feb 2025 05:37:39 GMT
• Title: Dynamic Pricing with Adversarially-Censored Demands
• Authors: Jianyu Xu, Yining Wang, Xi Chen, Yu-Xiang Wang,
• Abstract summary: We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,ldots, T$ is and dependent on the price. A perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. We show that our algorithm achieves $tildeO(sqrtT)$ optimal regret even with adversarial inventory series.
• Score: 25.566323930646178
• License:
• Abstract: We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,\ldots, T$ is stochastic and dependent on the price. However, a perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. To address this problem, we introduce a pricing algorithm based on the optimistic estimates of derivatives. We show that our algorithm achieves $\tilde{O}(\sqrt{T})$ optimal regret even with adversarial inventory series. Our findings advance the state-of-the-art in online decision-making problems with censored feedback, offering a theoretically optimal solution against adversarial observations.

Related papers

• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback [58.66941279460248]
  Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM) We study a model within this problem domain--contextual dueling bandits with adversarial feedback, where the true preference label can be flipped by an adversary. We propose an algorithm namely robust contextual dueling bandit (algo), which is based on uncertainty-weighted maximum likelihood estimation.
  arXiv  Detail & Related papers  (2024-04-16T17:59:55Z)
• Demand Balancing in Primal-Dual Optimization for Blind Network Revenue Management [6.72809363581332]
  This paper proposes a practically efficient algorithm with optimal theoretical regret which solves the classical network revenue management problem with unknown, nonparametric demand. A key technical contribution is the so-called demand balancing, which pairs the primal solution (i.e., the price) in each time period with another price to offset the violation of slackness on resource inventory constraints.
  arXiv  Detail & Related papers  (2024-04-06T01:39:51Z)
• Dynamic Pricing and Learning with Long-term Reference Effects [16.07344044662994]
  We study a simple and novel reference price mechanism where reference price is the average of the past prices offered by the seller. We show that under this mechanism, a markdown policy is near-optimal irrespective of the parameters of the model. We then consider a more challenging dynamic pricing and learning problem, where the demand model parameters are apriori unknown.
  arXiv  Detail & Related papers  (2024-02-19T21:36:54Z)
• Pricing with Contextual Elasticity and Heteroscedastic Valuation [23.96777734246062]
  We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity. Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.
  arXiv  Detail & Related papers  (2023-12-26T11:07:37Z)
• Contextual Dynamic Pricing with Strategic Buyers [93.97401997137564]
  We study the contextual dynamic pricing problem with strategic buyers. Seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. We propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue.
  arXiv  Detail & Related papers  (2023-07-08T23:06:42Z)
• Online Learning under Budget and ROI Constraints via Weak Adaptivity [57.097119428915796]
  Existing primal-dual algorithms for constrained online learning problems rely on two fundamental assumptions. We show how such assumptions can be circumvented by endowing standard primal-dual templates with weakly adaptive regret minimizers. We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions.
  arXiv  Detail & Related papers  (2023-02-02T16:30:33Z)
• Autoregressive Bandits [58.46584210388307]
  We propose a novel online learning setting, Autoregressive Bandits, in which the observed reward is governed by an autoregressive process of order $k$. We show that, under mild assumptions on the reward process, the optimal policy can be conveniently computed. We then devise a new optimistic regret minimization algorithm, namely, AutoRegressive Upper Confidence Bound (AR-UCB), that suffers sublinear regret of order $widetildemathcalO left( frac(k+1)3/2sqrtnT (1-G
  arXiv  Detail & Related papers  (2022-12-12T21:37:36Z)
• A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design [158.0041488194202]
  We study reserve price optimization in multi-phase second price auctions. From the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders. Third, the seller's per-step revenue is unknown, nonlinear, and cannot even be directly observed from the environment.
  arXiv  Detail & Related papers  (2022-10-19T03:49:05Z)
• Dynamic pricing and assortment under a contextual MNL demand [2.1320960069210475]
  We consider dynamic multi-product pricing and assortment problems under an unknown demand over T periods. We propose a randomized dynamic pricing policy based on a variant of the Online Newton Step algorithm (ONS) We also present a new optimistic algorithm for the adversarial MNL contextual bandits problem.
  arXiv  Detail & Related papers  (2021-10-19T14:37:10Z)
• Dynamic Pricing and Learning under the Bass Model [16.823029377470366]
  We develop an algorithm that satisfies a high probability regret guarantee of order $tilde O(m2/3)$; where the market size $m$ is known a priori. Unlike most regret analysis results, in the present problem the market size $m$ is the fundamental driver of the complexity.
  arXiv  Detail & Related papers  (2021-03-09T03:27:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.