Perturbed Pricing

TL;DR

This paper introduces a randomized pricing rule for revenue management that improves upon certainty equivalent pricing by incorporating controlled randomness, leading to better optimality and lower regret.

Contribution

It presents a novel randomized pricing method that enhances revenue management strategies beyond traditional certainty equivalent approaches.

Findings

Randomization around certainty equivalent prices improves revenue outcomes.

The proposed method achieves low regret in pricing decisions.

Randomized pricing outperforms deterministic rules in experiments.

Abstract

We propose a simple randomized rule for the optimization of prices in revenue management with contextual information. It is known that the certainty equivalent pricing rule, albeit popular, is sub-optimal. We show that, by allowing a small amount of randomization around these certainty equivalent prices, the benefits of optimal pricing and low regret are achievable.