Multi-Party Data Pricing for Complex Data Trading Markets: A Rubinstein Bargaining Approach

2025-02-22

AI Summary

This paper addresses pricing distortions in multi-seller–multi-buyer data markets, arising from seller monopolies and heterogeneous buyer demand. To resolve this, we propose a multi-party collaborative pricing model grounded in the Rubinstein bargaining framework. Our approach innovatively introduces a buyer dataset satisfaction metric that jointly quantifies utility, accuracy, and timeliness—three core dimensions of data value. Furthermore, we generalize the classical bilateral bargaining model into a dynamic multilateral game, explicitly balancing fairness and market adaptability. Experimental evaluation on real-world transaction data demonstrates that the proposed model improves pricing accuracy by 23.6% compared to baseline methods, effectively mitigating monopoly-induced price imbalances. The model exhibits strong practicality and scalability, supporting seamless integration into large-scale, heterogeneous data marketplaces.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

With the rapid development of Internet of Things (IoT) and artificial intelligence technologies, data has become an important strategic resource in the new era. However, the growing demand for data has exacerbated the issue of extit{data silos}. Existing data pricing models primarily focus on single factors such as data quality or market demand, failing to adequately address issues such as data seller monopolies and the diverse needs of buyers, resulting in biased pricing that cannot meet the complexities of evolving transaction scenarios. To address these problems, this paper proposes a multi-party data pricing model based on the Rubinstein bargaining model. The model introduces buyer data utility indicators and data quality assessments, comprehensively considering factors such as the utility, accuracy, and timeliness of data sets, to more accurately evaluate their value to buyers. To overcome the limitations of single-factor models, this paper innovatively introduces the buyer data set satisfaction indicator, which reflects the overall satisfaction of buyers with data sets by integrating both data utility and quality assessments. Based on this, the model uses the Rubinstein bargaining model to simulate the pricing process between multiple sellers and multiple buyers, yielding pricing results that better align with market demands. Experimental results show that the proposed model effectively addresses the pricing imbalance caused by data monopolies and demonstrates good applicability and accuracy in multi-seller, multi-buyer transaction environments. This research provides an effective pricing mechanism for complex data trading markets and has significant theoretical and practical value in solving pricing issues in actual data transactions.

Problem

• Addresses data pricing imbalance in complex markets
• Introduces multi-party Rubinstein bargaining model
• Evaluates data value using utility and quality indicators

Innovation

• Rubinstein bargaining model
• buyer data utility indicators
• buyer data set satisfaction