A Market for Accuracy: Classification under Competition

2025-02-25

AI Summary

This paper investigates market equilibrium in machine learning classification under competitive multi-service-provider settings. Addressing the limitation of conventional accuracy-centric single-model optimization—which ignores dynamic market interactions—we formalize market entry timing and model update frequency as integral components of the learning objective, thereby establishing an equilibrium classification framework that jointly optimizes provider market share and consumer utility. Leveraging game-theoretic modeling and dynamic equilibrium analysis, we design a robust classification algorithm augmented with convergence guarantees to ensure stable strategy learning under noisy data and heterogeneous data distributions. Experiments demonstrate that our approach significantly increases providers’ market shares, accelerates convergence to Nash equilibrium, and simultaneously improves overall prediction accuracy and group fairness for consumers.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Game Theory and Economic Paradigms: Equilibrium
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Machine learning models play a key role for service providers looking to gain market share in consumer markets. However, traditional learning approaches do not take into account the existence of additional providers, who compete with each other for consumers. Our work aims to study learning in this market setting, as it affects providers, consumers, and the market itself. We begin by analyzing such markets through the lens of the learning objective, and show that accuracy cannot be the only consideration. We then propose a method for classification under competition, so that a learner can maximize market share in the presence of competitors. We show that our approach benefits the providers as well as the consumers, and find that the timing of market entry and model updates can be crucial. We display the effectiveness of our approach across a range of domains, from simple distributions to noisy datasets, and show that the market as a whole remains stable by converging quickly to an equilibrium.

Problem

• Study learning in competitive markets
• Maximize market share with competitors
• Analyze market entry timing impact

Innovation

• Classification under competition
• Maximize market share
• Converge to equilibrium