Algorithms, Artificial Intelligence and Simple Rule Based Pricing

Abstract

The increasingly popular automated pricing strategies in e-commerce can be broadly categorized into two forms: simple rule-based algorithms, such as undercutting the lowest price, and more sophisticated artificial intelligence (AI) powered algorithms, like reinforcement learning (RL). RL algorithms are particularly appealing for pricing due to their potential ability to autonomously learn an optimal policy and adapt to changes in competitors' strategies and market conditions. Despite the common belief that RL algorithms hold a significant advantage over rule-based strategies, our extensive experiments, conducted under both a canonical Logit demand environment and a more realistic non-sequential search structural demand model, demonstrate that when competing against RL pricing algorithms, simple rule-based algorithms can lead to higher prices and benefit all sellers, compared to scenarios where multiple RL algorithms compete against each other. Theoretical analysis in a simplified setting yields consistent results. Our research sheds new light on the effectiveness of automated pricing algorithms and their interactions in competitive markets, providing practical insights for retailers in selecting appropriate pricing strategies.

Keywords: Algorithmic pricing, competition, rule-based pricing, reinforcement learning