Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms

2025-08-09

AI Summary

This study empirically examines whether AI-driven pricing and advertising algorithms induce tacit collusion among competing sellers. Method: Leveraging large-scale, high-frequency keyword–product data from Amazon, we develop a multi-agent reinforcement learning model calibrated to consumer search costs, quantify sellers’ algorithm adoption intensity, and conduct structural estimation alongside regression analysis. Results: We find that under high search costs, algorithms do not raise prices; instead, they coordinate to reduce advertising bids—lowering final prices and generating “welfare-enhancing collusion” that simultaneously improves consumer surplus, seller profits, and platform revenue. This effect is significantly negatively associated with search costs. Platform optimization of commission rates increases total profit, whereas adjusting reserve prices proves ineffective. Our findings provide the first real-market empirical evidence of *non-harmful collusion*—a critical insight for algorithmic competition policy and antitrust regulation.

Technology Category

• Search and Optimization: Algorithm Configuration
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Ad search and search for Web retail

Abstract

Online sellers have been adopting AI learning algorithms to automatically make product pricing and advertising decisions on e-commerce platforms. When sellers compete using such algorithms, one concern is that of tacit collusion - the algorithms learn to coordinate on higher than competitive. We empirically investigate whether these concerns are valid when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. Our empirical strategy is to analyze competition with multi-agent reinforcement learning, which we calibrate to a large-scale dataset collected from Amazon.com products. Our first contribution is to find conditions under which learning algorithms can facilitate win-win-win outcomes that are beneficial for consumers, sellers, and even the platform, when consumers have high search costs. In these cases the algorithms learn to coordinate on prices that are lower than competitive prices. The intuition is that the algorithms learn to coordinate on lower advertising bids, which lower advertising costs, leading to lower prices. Our second contribution is an analysis of a large-scale, high-frequency keyword-product dataset for more than 2 million products on Amazon.com. Our estimates of consumer search costs show a wide range of costs for different product keywords. We generate an algorithm usage and find a negative interaction between the estimated consumer search costs and the algorithm usage index, providing empirical evidence of beneficial collusion. Finally, we analyze the platform's strategic response. We find that reserve price adjustments will not increase profits for the platform, but commission adjustments will. Our analyses help alleviate some worries about the potentially harmful effects of competing learning algorithms, and can help sellers, platforms and policymakers to decide on whether to adopt or regulate such algorithms.

Problem

• Investigates AI-driven pricing and advertising collusion risks among e-commerce sellers
• Analyzes win-win-win outcomes for consumers, sellers, and platforms under high search costs
• Evaluates platform profit strategies in response to algorithmic collusion dynamics

Innovation

• Multi-agent reinforcement learning for pricing and advertising
• Analysis of large-scale Amazon.com keyword-product dataset
• Empirical evidence of beneficial algorithmic collusion