Balancing Profit and Fairness in Risk-Based Pricing Markets

2025-05-30

AI Summary

This paper addresses the systemic exclusion of disadvantaged groups from essential resources—such as health insurance and consumer credit—in dynamic risk-pricing markets. To reconcile corporate profitability with social equity, we propose an interpretable, tax-based coordination mechanism. Methodologically, we introduce *local population disparity constraints* to ensure global exit fairness; design an L1-regularized, interpretable reinforcement learning social planner that achieves Pareto improvements without centralized coordination; and integrate MarketSim multi-agent simulation with linear prior modeling to derive a tiered taxation scheme. Empirical evaluation in health insurance and consumer credit settings demonstrates a 16% improvement in demand fairness and significantly higher aggregate social welfare compared to fixed linear taxation—thereby achieving a win–win balance between allocative efficiency and distributive fairness.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• 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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and consumer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's emph{local} demographic gap implicitly bounds the emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce exttt{MarketSim} -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an $mathcal{L}_1$ regularizer. The learned policy is thus both transparent and easily interpretable. In two empirically calibrated markets, i.e., U.S. health-insurance and consumer-credit, our planner simultaneously raises demand-fairness by up to $16%$ relative to unregulated Free Market while outperforming a fixed linear schedule in terms of social welfare without explicit coordination. These results illustrate how AI-assisted regulation can convert a competitive social dilemma into a win-win equilibrium, providing a principled and practical framework for fairness-aware market oversight.

Problem

• Dynamic risk-based pricing excludes vulnerable consumer groups
• Regulator aligns private incentives with social objectives via interpretable tax
• AI-assisted regulation improves fairness and welfare in competitive markets

Innovation

• Learned interpretable tax schedule for fairness
• MarketSim simulator for heterogeneous consumers
• Reinforcement learning for social welfare optimization