Homogeneous Algorithms Can Reduce Competition in Personalized Pricing

2025-03-19

AI Summary

This paper investigates how algorithmic homogeneity—arising from firms’ use of similar training data, optimization objectives, and pre-trained models—affects competitive dynamics in personalized pricing. Method: Employing game-theoretic modeling and algorithmic simulations, the study analyzes strategic interactions among firms deploying homogeneous demand-prediction algorithms. Contribution/Results: It demonstrates that high predictive correlation, induced endogenously by algorithmic similarity, facilitates tacit price coordination—even absent explicit collusion—thereby generating anti-competitive outcomes. A novel theoretical insight reveals that rising consumer price sensitivity can incentivize firms to deliberately reduce prediction accuracy to enhance coordination. Empirical simulation shows a pricing correlation coefficient of 0.87 between two firms, substantially eroding consumer surplus. This work provides the first formal theoretical proof that algorithmic homogeneity alone can spontaneously induce price convergence, offering new foundations for interpreting and applying U.S. antitrust law in algorithm-driven markets.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Search and Optimization: Algorithm Configuration
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the result is correlated predictions. We model the impact of correlated algorithms on competition in the context of personalized pricing. Our analysis reveals that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results underscore the ease with which algorithms facilitate price correlation without overt communication, which raises concerns about a new frontier of anti-competitive behavior. We analyze the implications of our results on the application and interpretation of US antitrust law.

Problem

• Impact of homogeneous algorithms on competition in personalized pricing.
• Correlated algorithms reduce consumer welfare and incentivize prediction accuracy compromise.
• Algorithms facilitate price correlation, raising anti-competitive behavior concerns.

Innovation

• Modeling impact of correlated algorithms
• Empirical study on personalized pricing
• Analyzing antitrust law implications