提出数据与模型联合定价机制，实现三方公平共赢。
TripleWin: Fixed-Point Equilibrium Pricing for Data-Model Coupled Markets
- 构建供需双向闭环，统一处理数据与模型交易
- 基于沙普利值分配收益，实现价格全局收敛
- 相比中介主导方案更公平，适合多方协同场景
机器学习模型经济的发展使得训练数据集与预训练模型市场紧密耦合。然而，现有定价方法多将数据与模型交易割裂，或依赖中介主导的流程，偏向某一方利益。已有研究虽考虑买家外部性，但未能在数据卖家、模型生产者与模型买家之间建立同时且对称的机制。本文提出一个统一的数据-模型耦合市场，将数据与模型交易视为单一系统。供给侧映射将数据支付转化为买方可见的模型报价，需求侧映射通过沙普利值分配将买方价格回传至数据集。二者形成闭环，连接四类交互：供需双向传播及买家间、卖家间的相互耦合。我们证明该联合算子为标准干扰函数（SIF），确保均衡价格的存在性、唯一性与全局收敛性。实验表明，该机制具备高效收敛性与更高公平性，优于中介主导及单边基准方案。代码已开源：https://github.com/HongrunRen1109/Triple-Win-Pricing。
原文摘要 · Abstract (English)
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.
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