Fairness-aware Online Price Discrimination with Nonparametric Demand Models

• URL: http://arxiv.org/abs/2111.08221v2
• Date: Fri, 28 Jul 2023 14:22:38 GMT
• Title: Fairness-aware Online Price Discrimination with Nonparametric Demand Models
• Authors: Xi Chen, Jiameng Lyu, Xuan Zhang, Yuan Zhou
• Abstract summary: This paper studies the problem of dynamic discriminatory pricing under fairness constraints. We propose an optimal dynamic pricing policy regarding regret, which enforces the strict price fairness constraint.
• Score: 13.46602731592102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concerns about fairness, which even violates the regulation and laws. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length $T$ for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. This work adopts the fairness notion from Cohen et al. (2022). For price fairness, we propose an optimal dynamic pricing policy regarding regret, which enforces the strict price fairness constraint. In contrast to the standard $\sqrt{T}$-type regret in online learning, we show that the optimal regret in our case is $\tilde{O}(T^{4/5})$. We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret. We also demonstrate that our algorithmic techniques can be adapted to more general scenarios such as fairness among multiple groups of customers.

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)
• Fair Allocation in Dynamic Mechanism Design [57.66441610380448]
  We consider a problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group.
  arXiv  Detail & Related papers  (2024-05-31T19:26:05Z)
• 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)
• 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)
• Dynamic Pricing and Learning with Bayesian Persuasion [18.59029578133633]
  We consider a novel dynamic pricing and learning setting where in addition to setting prices of products, the seller also ex-ante commits to 'advertising schemes' We use the popular Bayesian persuasion framework to model the effect of these signals on the buyers' valuation and purchase responses. We design an online algorithm that can use past purchase responses to adaptively learn the optimal pricing and advertising strategy.
  arXiv  Detail & Related papers  (2023-04-27T17:52:06Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• 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)
• Doubly Fair Dynamic Pricing [14.28146588978302]
  We study the problem of online dynamic pricing with two types of fairness constraints. A policy that is simultaneously procedural and substantive fair is referred to as "doubly fair" This is the first dynamic pricing algorithm that learns to price while satisfying two fairness constraints at the same time.
  arXiv  Detail & Related papers  (2022-09-23T20:02:09Z)
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation [55.0391061198924]
  Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. It is nontrivial to incorporate price preferences for session-based recommendation. We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
  arXiv  Detail & Related papers  (2022-05-09T10:47:15Z)
• Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids [9.125875181760625]
  We propose a realistic multiple dynamic pricing approach to demand response in the retail market. The proposed framework is evaluated via simulations based on real-world datasets.
  arXiv  Detail & Related papers  (2021-06-10T16:47:15Z)

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.