感知价值不确定下数据产品交易抽样策略分析

Analysis of Sampling Strategy for Data Product Trading under Perceived Value Uncertainty

针对数据产品交易前数据需求者对价值认知的不确定,探究数据产品交易抽样策略成为改善数据交易平台活跃度和数据交易量的重要途径。整合双寡头竞争博弈模型和扩展的两维Hotelling模型,通过刻画数据产品交易抽样策略对数据需求者效用的影响,本文将数据交易市场划分为以数据匹配为主导和以数据质量为主导的市场,探究差异化数据交易平台不同抽样策略情景下的最优数据产品质量及相应的交易价格。此外,本文还对数据交易抽样策略与均衡状态下数据产品质量及平台收益间关系进行分析。研究表明:相比不采取数据交易抽样策略和都采取抽样策略,当且仅当满足特定条件时仅一个平台采取数据抽样策略更有利于提升平台自身及竞争者的收益。研究结论为数据交易平台采取数据抽样策略提供了理论指导,同时对优化数据产品质量和定价提供了借鉴。

In view of the uncertainty of data demanders'perception of the value of data products before data trading,exploring the sampling strategy of data product trading has become an important way to improve the activity of data trading platform and data trading volume.Based on the duopoly competition game model and the extended two-dimensional Hotelling model,this paper divides the data trading market into data-matching-oriented and data-quality-oriented markets,and explores the optimal data product quality and corresponding trading prices by describing the influ-ence of data product trading sampling strategy on the utility of data demanders.In addition,this paper also analyzes the relationship between data trading sampling strategy and the quality of data products and platform revenue in equilibrium.The research shows that:compared with no adopting the sampling strategy and adopting sampling strategies,if and only when certain conditions are met,sampling strategy adopted by a platform is more conducive to improving the revenue of itself and competitors.The research conclusions provide theoretical guidance for data trading platforms to adopt sampling strategies,and provide references for optimizing the quality and pricing of data products on the platform.