# Reinforcement Learning for Fair Dynamic Pricing

arXiv: https://arxiv.org/abs/1803.09967 · HTML: https://mlsanity.com/paper/1803.09967/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Doubly Fair Dynamic Pricing](https://mlsanity.com/paper/2209.11837/) (2022)
- [Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness](https://mlsanity.com/paper/2102.00417/) (2021)
- [Sales Time Series Analytics Using Deep Q-Learning](https://mlsanity.com/paper/2201.02058/) (2022)
- [Analytics of Business Time Series Using Machine Learning and Bayesian Inference](https://mlsanity.com/paper/2205.12905/) (2022)
- [Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework](https://mlsanity.com/paper/2307.11379/) (2023)
- [Multi-Agent Dynamic Pricing in a Blockchain Protocol Using Gaussian Bandits](https://mlsanity.com/paper/2212.07942/) (2022)
- [Text-Based Product Matching -- Semi-Supervised Clustering Approach](https://mlsanity.com/paper/2402.10091/) (2024)
- [Fairness Incentives in Response to Unfair Dynamic Pricing](https://mlsanity.com/paper/2404.14620/) (2024)
- [Balancing Profit and Fairness in Risk-Based Pricing Markets](https://mlsanity.com/paper/2506.00140/) (2025)

## Abstract (arXiv)

Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid unfair price differences between groups of customers. This paper shows how to solve dynamic pricing by using Reinforcement Learning (RL) techniques so that prices are maximized while keeping a balance between revenue and fairness. We demonstrate that RL provides two main features to support fairness in dynamic pricing: on the one hand, RL is able to learn from recent experience, adapting the pricing policy to complex market environments; on the other hand, it provides a trade-off between short and long-term objectives, hence integrating fairness into the model's core. Considering these two features, we propose the application of RL for revenue optimization, with the additional integration of fairness as part of the learning procedure by using Jain's index as a metric. Results in a simulated environment show a significant improvement in fairness while at the same time maintaining optimisation of revenue.