TripleWin: Fixed-Point Equilibrium Pricing for Data-Model Coupled Markets

• URL: http://arxiv.org/abs/2511.03368v1
• Date: Wed, 05 Nov 2025 11:20:14 GMT
• Title: TripleWin: Fixed-Point Equilibrium Pricing for Data-Model Coupled Markets
• Authors: Hongrun Ren, Yun Xiong, Lei You, Yingying Wang, Haixu Xiong, Yangyong Zhu,
• Abstract summary: We propose a unified data-model coupled market that treats dataset and model trading as a single system.<n>A supply-side mapping transforms dataset payments into buyer-visible model quotations, while a demand-side mapping propagates buyer prices back to datasets.<n>We prove that the joint operator is a standard interference function (SIF), guaranteeing existence, uniqueness, and global convergence of equilibrium prices.
• Score: 11.356761727022183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Sustainable AI Economy Needs Data Deals That Work for Generators [56.949279542190084]
  We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality.<n>We analyze 73 public data deals and show that the majority of value accrues to aggregators.<n>We propose an Equitable Data-Value Exchange Framework to enable a minimal market that benefits all participants.
  arXiv  Detail & Related papers  (2026-01-15T01:05:48Z)
• A Tale of Two Experts: Cooperative Learning for Source-Free Unsupervised Domain Adaptation [59.88864205383671]
  Source-Free Unsupervised Domain Adaptation (SFUDA) addresses the realistic challenge of adapting a source-trained model to a target domain without access to the source data.<n>Existing SFUDA methods either exploit only the source model's predictions or fine-tune large multimodal models.<n>We propose the Experts Cooperative Learning (EXCL) to exploit complementary insights and the latent structure of target data.
  arXiv  Detail & Related papers  (2025-09-26T11:39:50Z)
• Data Pricing for Graph Neural Networks without Pre-purchased Inspection [15.556650640576311]
  Model marketplaces leverage model trading mechanisms to properly incentive data owners to contribute their data.<n>We propose a novel mechanism, named Structural Importance based Model Trading (SIMT) mechanism, that assesses the data importance and compensates data owners accordingly.<n>SIMT consistently outperforms vanilla baselines by up to $40%$ in both MacroF1 and MicroF1.
  arXiv  Detail & Related papers  (2025-02-12T10:42:04Z)
• An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
  Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. We frame FL as a marketplace of models, where clients act as both buyers and sellers. We propose an auction-based solution to ensure proper pricing based on performance gain.
  arXiv  Detail & Related papers  (2024-02-02T07:25:53Z)
• Equilibrium of Data Markets with Externality [5.383900608313559]
  We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers. A key component here is the negative externality buyers induce on one another due to data purchases. We prove that platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees.
  arXiv  Detail & Related papers  (2023-02-16T00:57:49Z)
• Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition [98.25592165484737]
  We propose a more effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL) CMPL achieves $17.6%$ and $25.1%$ Top-1 accuracy on Kinetics-400 and UCF-101 using only the RGB modality and $1%$ labeled data, respectively.
  arXiv  Detail & Related papers  (2021-12-17T18:59:41Z)
• A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data [24.847898465750667]
  An emerging paradigm in Machine Learning (ML) is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data. The problem of valuation of models, as well as the questions of incentives for collaborative training and trading of data/models, have received limited treatment in the literature. In this paper, a new ecosystem of ML model trading over a trusted ML-based network is proposed. The buyer can acquire the model of interest from the ML market, and interested sellers spend local computations on their data to enhance that model's quality
  arXiv  Detail & Related papers  (2021-12-06T08:52:42Z)
• Data Sharing Markets [95.13209326119153]
  We study a setup where each agent can be both buyer and seller of data. We consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money)
  arXiv  Detail & Related papers  (2021-07-19T06:00:34Z)
• Competition analysis on the over-the-counter credit default swap market [0.0]
  We study the competition between central counterparties through collateral requirements. We present models that successfully estimate the initial margin requirements. Second, we model counterpart choice on the interdealer market using a novel semi-supervised predictive task.
  arXiv  Detail & Related papers  (2020-12-03T13:02:53Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

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.