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| 论文编号： | 15414 | |
| 作者编号： | 2120223824 | |
| 上传时间： | 2025/6/10 20:43:53 | |
| 中文题目： | 基于数据潜在价值的数据定价研究 | |
| 英文题目： | Research on Data Pricing Based on Potential Value | |
| 指导老师： | 徐曼 | |
| 中文关键字： | 数据定价，数据潜在价值，数据交易，博弈论 | |
| 英文关键字： | Data Pricing, Potential Data Value, Data Exchange Platform，Game Theory | |
| 中文摘要： | 在数据要素市场化进程不断深化的背景下，构建科学合理的数据定价机制已成为推动数据资源高效流通与价值实现的重要课题。针对现有定价方法中普遍存在的定价依据不透明、模型适应性差以及无法有效刻画数据潜在价值的问题，本文立足于数据自身特征，提出一种基于潜在价值的数据交易定价研究框架，试图在理论与实践层面为数据资产定价提供可操作性较强的支持路径。 本文首先从数据来源、数据量、数据类型、发布时间、准确性与可靠性六个维度出发，构建了多指标数据价值评估模型，采用层次分析法（AHP），确保指标体系的结构合理性与权重分配的科学性。在此基础上，结合数据资产的使用特性与不确定性收益属性，进一步引入重置成本法与实物期权法，构建数据价格上下限的定价区间模型，为数据交易提供具有弹性与参考性的估值边界。 为进一步完善定价机制，本文设计了一种基于定价区间的一对一博弈定价模型。在明确数据价格上下限基础上，结合实际交易中买卖双方议价次数有限、策略信息不完全等特点，设定博弈过程。模型通过设定不同报价点在定价区间内的位置，并结合预设成交概率函数，分析卖方每一轮中的最优报价选择及其收益表现，为数据提供方制定实际交易中的定价策略提供理论参考。最后，本文以淘宝电商平台广告数据为研究对象开展实证分析，验证了所建模型在典型数据交易场景中的可行性与有效性。研究结果不仅为数据交易平台的定价机制设计提供了理论支撑，也为数据要素市场的规范发展与制度建设提供了实践参考。 | |
| 英文摘要： | With the continuous advancement of the market-oriented reform of data as a production factor, establishing a scientific and reasonable pricing mechanism for data has become a crucial issue for promoting efficient circulation and value realization of data resources. In view of the common problems in existing pricing methods—such as lack of transparency in pricing criteria, poor model adaptability, and failure to effectively capture the potential value of data—this study proposes a data transaction pricing framework based on latent value, aiming to provide a more practical and operable path for data asset pricing both theoretically and practically. First, a multi-criteria data value evaluation model is constructed from six dimensions: data source, data volume, data type, publication time, accuracy, and reliability. The Analytic Hierarchy Process (AHP) is applied to ensure the structural rationality of the indicator system and the scientific allocation of weights. On this basis, considering the usage characteristics and uncertain return attributes of data assets, the reset cost method and the real option method are introduced to establish a pricing interval model with upper and lower bounds, providing flexible and referential value boundaries for data transactions. To further improve the pricing mechanism, this study designs a one-to-one bargaining pricing model based on the defined pricing interval. Taking into account the limited number of bargaining rounds and incomplete strategic information in real-world negotiations, the model simulates the bargaining process. By setting different offer points within the pricing interval and incorporating a predefined transaction probability function, the model analyzes the optimal pricing decisions and expected returns of the seller in each round, offering theoretical support for data providers to formulate pricing strategies in actual transactions. Finally, a case study using advertising data from the Taobao e-commerce platform is conducted to empirically validate the feasibility and effectiveness of the proposed model in typical data transaction scenarios. The findings provide theoretical support for pricing mechanism design on data trading platforms and offer practical insights for the standardized development and institutional construction of the data factor market. | |
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