Regulatory Instruments for Fair Personalized Pricing
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Abstract (arXiv)
Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors. It has become common practice in many industries nowadays due to the availability of a growing amount of high granular consumer data. The discriminatory nature of personalized pricing has triggered heated debates among policymakers and academics on how to design regulation policies to balance market efficiency and equity. In this paper, we propose two sound policy instruments, i.e., capping the range of the personalized prices or their ratios. We investigate the optimal pricing strategy of a profit-maximizing monopoly under both regulatory constraints and the impact of imposing them on consumer surplus, producer surplus, and social welfare. We theoretically prove that both proposed constraints can help balance consumer surplus and producer surplus at the expense of total surplus for common demand distributions, such as uniform, logistic, and exponential distributions. Experiments on both simulation and real-world datasets demonstrate the correctness of these theoretical results. Our findings and insights shed light on regulatory policy design for the increasingly monopolized business in the digital era.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 67 references that are in the map. First posted on arXiv 9 Feb 2022.
Builds on
20 of 67 references.
References · 20
- 2016Equality of Opportunity in Supervised Learningcited by 2,000
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
- 2017Counterfactual Fairnesscited by 802
- 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
- 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
- 2017Avoiding Discrimination through Causal Reasoningcited by 191
- 2018Path-Specific Counterfactual Fairnesscited by 179
- 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
- 2016Dynamic Pricing in High-dimensionscited by 55
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- 2019Fair Classification and Social Welfarecited by 40
- 2020SenSeI: Sensitive Set Invariance for Enforcing Individual Fairnesscited by 37
8 more
- 2016Dynamic Pricing with Demand Covariatescited by 30
- 2020Model Distillation for Revenue Optimization: Interpretable Personalized Pricingcited by 24
- 2021Distribution-free Contextual Dynamic Pricingcited by 22
- 2017Revenue Optimization with Approximate Bid Predictionscited by 21
- 2020Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learningcited by 17
- 2020Fairness, Welfare, and Equity in Personalized Pricingcited by 17
- 2020Algorithmic Decision Making with Conditional Fairnesscited by 14
- 2019Algorithmic Price Discriminationcited by 1
Built on by
Citing papers per year: 2022: 2; 2023: 2.
4 papers.
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- 2022Product Ranking for Revenue Maximization with Multiple Purchasescited by 3
- 2023Pareto Invariant Representation Learning for Multimedia Recommendationcited by 3
- 2022Causal Intervention for Fairness in Multi-behavior Recommendationcited by 0
- 2023Pareto Invariant Representation Learning for Multimedia Recommendation9 Aug 2023
- 2023What Is Missing in IRM Training and Evaluation? Challenges and Solutions4 Mar 2023
- 2022Product Ranking for Revenue Maximization with Multiple Purchases15 Oct 2022
- 2022Causal Intervention for Fairness in Multi-behavior Recommendation10 Sep 2022