Balancing Profit and Fairness in Risk-Based Pricing Markets

• URL: http://arxiv.org/abs/2506.00140v2
• Date: Wed, 04 Jun 2025 16:06:36 GMT
• Title: Balancing Profit and Fairness in Risk-Based Pricing Markets
• Authors: Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, Golnoosh Farnadi,
• Abstract summary: We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule.<n>In two empirically calibrated markets, i.e., U.S. health-insurance and consumer-credit, our planner simultaneously raises demand-fairness by up to $16%$.
• Score: 7.991187769447732
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \texttt{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.

Related papers

• Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets [44.675845572303324]
  We introduce Conditional Value-at-Risk as a consumer-side objective that compresses group-level utility disparities.<n>Our experiments reveal that the "free fairness" regime, where producer constraints impose no consumer cost, disappears in multi item settings.<n>Strikingly, moderate fairness constraints can improve business metrics by diversifying exposure away from saturated producers.
  arXiv  Detail & Related papers  (2026-02-11T10:59:32Z)
• Fairness Aware Reward Optimization [78.85867531002346]
  We introduce Fairness Aware Reward Optimization (Faro), an in-processing framework that trains reward models under demographic parity, equalized odds, or counterfactual fairness constraints.<n>We provide the first theoretical analysis of reward-level fairness in LLM alignment.<n>Faro significantly reduces bias and harmful generations while maintaining or improving model quality.
  arXiv  Detail & Related papers  (2026-02-08T03:35:49Z)
• A Unifying Human-Centered AI Fairness Framework [2.9385229328767988]
  We introduce a unifying human-centered fairness framework that covers eight distinct fairness metrics.<n>Rather than privileging a single fairness notion, the framework enables stakeholders to assign weights across multiple fairness objectives.<n>We show that adjusting weights reveals nuanced trade-offs between different fairness metrics.
  arXiv  Detail & Related papers  (2025-12-07T17:52:38Z)
• Fair-GNE : Generalized Nash Equilibrium-Seeking Fairness in Multiagent Healthcare Automation [0.0]
  Existing multi-agent reinforcement learning approaches steer fairness by shaping reward through post hoc orchestrations.<n>We address this shortcoming with a learning enabled optimization scheme among self-interested decision makers.<n>Our results communicate our formulations, evaluation metrics, and equilibrium-seeking innovations in large multi-agent learning-based healthcare systems.
  arXiv  Detail & Related papers  (2025-11-18T04:48:50Z)
• Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare [87.06241096619112]
  Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.<n>We introduce the Social Welfare Function Benchmark, a dynamic simulation environment where an LLM acts as a sovereign allocator.<n>We evaluate 20 state-of-the-art LLMs and present the first leaderboard for social welfare allocation.
  arXiv  Detail & Related papers  (2025-10-01T17:52:31Z)
• Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets [3.7321070110102075]
  Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems.<n>A fairness-aware multiagent reinforcement learning framework, FairMarket-RL, is proposed.<n>The framework shifts exchanges toward local P2P trades, lowers consumer costs relative to grid-only procurement, sustains strong fairness across participants, and preserves utility viability.
  arXiv  Detail & Related papers  (2025-08-26T02:25:17Z)
• FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets [1.7284653203366598]
  This paper presents FairMarket-RL, a novel framework that combines Large Language Models (LLMs) with Reinforcement Learning (RL) to enable fairness-aware trading agents.<n>In a simulated P2P microgrid with multiple sellers and buyers, the LLM acts as a real-time fairness critic, evaluating each trading episode using two metrics: Fairness-To-Buyer (FTB) and Fairness-Between-Sellers (FBS)
  arXiv  Detail & Related papers  (2025-06-28T01:17:55Z)
• Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact [0.0]
  We quantify how different fairness interventions impact profit margins and default rates.<n>We identify the specific economic conditions under which fair lending becomes profitable.<n>These findings offer practical guidance for designing lending algorithms that balance ethical considerations with business objectives.
  arXiv  Detail & Related papers  (2025-05-08T19:18:33Z)
• Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics [96.68144350976637]
  Fairness is an increasingly important factor in re-ranking tasks.<n>The accuracy-fairness trade-off parallels the coupling of the commodity tax transfer process.<n>We introduce the Elastic Fairness Curve (EF-Curve) as an evaluation framework.<n>We also propose ElasticRank, a fair re-ranking algorithm that employs elasticity calculations to adjust inter-item distances.
  arXiv  Detail & Related papers  (2025-04-21T09:41:08Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
  We analyze the tension between fairness and accuracy from a causal lens for the first time.<n>We show that enforcing a causal constraint often reduces the disparity between demographic groups.<n>We introduce a new neural approach for causally-constrained fair learning.
  arXiv  Detail & Related papers  (2024-05-24T11:19:52Z)
• Fairness Incentives in Response to Unfair Dynamic Pricing [7.991187769447732]
  We design a basic simulated economy, wherein we generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours. To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and as a full reinforcement learning (RL) problem. We find that social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings.
  arXiv  Detail & Related papers  (2024-04-22T23:12:58Z)
• Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning [151.03738099494765]
  We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. We propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model.
  arXiv  Detail & Related papers  (2023-02-24T17:16:27Z)
• Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations [16.48389671789281]
  We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market. By playing against each other, our deep-reinforcement-learning-driven agents learn emergent behaviors. We show convergence rates for our multi-agent policy gradient algorithm under a transitivity assumption.
  arXiv  Detail & Related papers  (2022-10-13T17:06:08Z)
• Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models [73.24381010980606]
  This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the IRS. We show how the use of more flexible machine learning methods for selecting audits may affect vertical equity. Our results have implications for the design of algorithmic tools across the public sector.
  arXiv  Detail & Related papers  (2022-06-20T16:27:06Z)
• Regulatory Instruments for Fair Personalized Pricing [34.986747852934634]
  We investigate the optimal pricing strategy of a profit-maximizing monopoly under both regulatory constraints and the impact of imposing them on consumer surplus, producer surplus, and social welfare. Our findings and insights shed light on regulatory policy design for the increasingly monopolized business in the digital era.
  arXiv  Detail & Related papers  (2022-02-09T03:07:08Z)
• Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness [4.94950858749529]
  Two notions of fairness might be to a) charge each individual the same price or b) charge each individual according to the cost that they bring to the pool. We show that it is possible for both groups (high and low risk) to strictly benefit by joining an insurance pool where costs are evenly split, as opposed to being in separate risk pools. We build on this by producing a pricing scheme that maximally subsidizes the high risk group, while maintaining an incentive for lower risk people to stay in the insurance pool.
  arXiv  Detail & Related papers  (2021-02-27T22:55:33Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.