Pricing AI Model Accuracy

2025-04-17

AI Summary

This paper examines the trade-off between firms’ accuracy investment strategies and social welfare in an AI model duopoly market. Recognizing heterogeneous consumer preferences over false positive (FP) and false negative (FN) rates, we develop a game-theoretic model that decomposes prediction error and formally identify dimensional asymmetry in accuracy investment: firms maximize profit only by investing in the error dimension—FP or FN—in which they hold a relative advantage; indiscriminate improvement of overall accuracy can reduce profits. We prove theoretically that while such targeted investment decreases consumer surplus, it mitigates excessive competition and thereby increases total social welfare—a Pareto improvement. Numerical analysis quantifies the magnitude of this effect. Our core contribution lies in challenging the conventional “higher accuracy is always better” intuition, offering a novel paradigm for AI market regulation and accuracy-based pricing mechanisms.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

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

This paper examines the market for AI models in which firms compete to provide accurate model predictions and consumers exhibit heterogeneous preferences for model accuracy. We develop a consumer-firm duopoly model to analyze how competition affects firms' incentives to improve model accuracy. Each firm aims to minimize its model's error, but this choice can often be suboptimal. Counterintuitively, we find that in a competitive market, firms that improve overall accuracy do not necessarily improve their profits. Rather, each firm's optimal decision is to invest further on the error dimension where it has a competitive advantage. By decomposing model errors into false positive and false negative rates, firms can reduce errors in each dimension through investments. Firms are strictly better off investing on their superior dimension and strictly worse off with investments on their inferior dimension. Profitable investments adversely affect consumers but increase overall welfare.

Problem

• Analyzes competition in AI model accuracy markets
• Examines firms' incentives to improve prediction accuracy
• Studies optimal error investment strategies for profits

Innovation

• Consumer-firm duopoly model analyzes competition effects
• Invest in superior error dimension for optimal profits
• Decompose errors into false positives and negatives