The rise of the machine learning (ML) model economy has intertwined markets for training datasets and pre-trained models. However, most pricing approaches still separate data and model transactions or rely on broker-centric pipelines that favor one side. Recent studies of data markets with externalities capture buyer interactions but do not yield a simultaneous and symmetric mechanism across data sellers, model producers, and model buyers. We propose a unified data-model coupled market that treats dataset and model trading as a single system. A supply-side mapping transforms dataset payments into buyer-visible model quotations, while a demand-side mapping propagates buyer prices back to datasets through Shapley-based allocation. Together, they form a closed loop that links four interactions: supply-demand propagation in both directions and mutual coupling among buyers and among sellers. We prove that the joint operator is a standard interference function (SIF), guaranteeing existence, uniqueness, and global convergence of equilibrium prices. Experiments demonstrate efficient convergence and improved fairness compared with broker-centric and one-sided baselines. The code is available on https://github.com/HongrunRen1109/Triple-Win-Pricing.

翻译：机器学习（ML）模型经济的兴起使得训练数据集与预训练模型的市场相互交织。然而，大多数定价方法仍将数据和模型交易分离，或依赖于偏向一方的以经纪商为中心的流程。近期关于具有外部性的数据市场的研究虽捕捉了买方间的相互作用，但未能形成一种在数据卖方、模型生产者和模型买方之间同时且对称的机制。我们提出了一个统一的数据-模型耦合市场，将数据集和模型交易视为单一系统。一个供给侧映射将数据集支付转化为买方可见的模型报价，而一个需求侧映射则通过基于Shapley值的分配将买方价格反向传播至数据集。二者共同构成一个闭环，连接了四个相互作用：双向的供需传播，以及买方之间与卖方之间的相互耦合。我们证明了该联合算子是一个标准干扰函数（SIF），从而保证了均衡价格的存在性、唯一性和全局收敛性。实验表明，与以经纪商为中心及单侧基准方法相比，该方法实现了高效收敛并提升了公平性。代码可在 https://github.com/HongrunRen1109/Triple-Win-Pricing 获取。