Reinforcement Learning for Fair Dynamic Pricing

TL;DR

This paper presents a reinforcement learning approach to dynamic pricing that balances revenue maximization with fairness, using Jain's index to measure fairness and demonstrating improved fairness without sacrificing revenue in simulations.

Contribution

It introduces a novel RL-based method that incorporates fairness directly into the pricing optimization process, balancing short-term revenue and long-term fairness.

Findings

Significant fairness improvements in simulated environments

Maintained revenue optimization while enhancing fairness

RL adapts to complex market conditions effectively

Abstract

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.