标题： 基于风险定价市场的盈利与公平的平衡 显示英文标题

标题： Balancing Profit and Fairness in Risk-Based Pricing Markets

摘要： 动态的基于风险的定价可能会系统性地将脆弱的消费者群体排除在健康保险和消费信贷等基本资源之外。 我们表明，监管者可以通过学习到的、可解释的税收计划重新调整私人激励与社会目标之间的关系。 首先，我们提供了一个正式命题：限制每个公司的\emph{局部的}种族差距实际上会隐式地限制\emph{全局的}退出差异，从而激励公司层面的惩罚。 在此基础上，我们引入了\texttt{市场模拟}——一个开源、可扩展的异质消费者和利润最大化的公司模拟器，并训练了一种强化学习（RL）社会规划者（SP），它通过$\mathcal{L}_1$正则化器选择了一个带括号的公平税，同时尽量接近一个简单的线性先验。 因此，所学到的政策既透明又易于解释。 在两个经过实证校准的市场中，即美国的健康保险和消费信贷市场，我们的规划者相对于不受管制的自由市场，能够提高高达$16\%$的需求公平性，同时在社会福利方面优于固定的线性时间表，而无需明确协调。 这些结果展示了如何通过人工智能辅助的监管将竞争性的社会困境转化为双赢的均衡，为公平意识的市场监管提供了原则性和实用性的框架。 显示英文摘要

摘要： 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.