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

By Ruben Van De Geer et al

Published on April 11, 2018

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Table of Contents

Abstract
Keywords
1. Introduction
1.1 Motivation
1.2 Literature
1.3 Contributions
2. Experimental Design
2.1 Experimental Setting
2.2 Competitor Algorithms

Summary

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. The challenge focused on pricing and demand learning algorithms in simulated market environments. The study found that the relative performance of algorithms varies across different market dynamics, highlighting the complexity of pricing and learning in competitive settings. The paper contributes insights into well-performing pricing strategies with learning and competition, emphasizing the importance of understanding market dynamics that are not analytically tractable. The experiments revealed various findings, including the impact of competition on pricing strategies, exploration strategies, and the performance variation across different market structures. Overall, the study aims to enhance understanding of pricing and learning in competitive environments and guide future research.