# Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference

arXiv: https://arxiv.org/abs/1804.03219 · HTML: https://mlsanity.com/paper/1804.03219/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Supracompetitive Pricing Under AI Monoculture](https://mlsanity.com/paper/2601.01279/) (2026)
- [Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing](https://mlsanity.com/paper/2207.06492/) (2022)
- [Algorithmic Predation: Equilibrium Analysis in Dynamic Oligopolies with Smooth Market Sharing](https://mlsanity.com/paper/2510.27008/) (2025)

## Abstract (arXiv)

This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.