数据要素的市场化价格形成与智能定价  

Market-oriented Pricing Formation and Intelligent Pricing of Data Elements

作　　者：沈俊鑫[1] 伊文霞 SHEN Junxin;YI Wenxia

机构地区：[1]昆明理工大学管理与经济学院,云南昆明650093 [2]西南林业大学经济管理学院,云南昆明650224

出　　处：《思想战线》2025年第2期163-172,共10页Thinking

基　　金：国家自然科学基金项目“基于人工智能的数据要素市场化价格形成、定价方法与决策仿真研究”阶段性成果(72464018);云南省哲学社会科学“数据要素赋能新质生产力”创新团队阶段性成果(2025CX15)。

摘　　要：数据要素具有虚拟性、异质性、非竞争性等特性,传统的要素定价理论和方法无法适用于数据要素市场,定价难问题已严重阻碍数据要素的市场化发展。数据交易是实现数据要素市场化的主要途径,数据要素的价格形成包括市场化定价机制和价格发现机制。传统的经济学定价范式以价值为基础,而“数智赋能”定价可基于数据直接发现定价变量的关系,并提供差异化、个性化的动态定价。智能科学定价范式经历了从模型驱动、数据驱动、双驱动到大模型驱动的转变,基于智能科学的定价策略不仅兼顾了使用场景、用户异质性,还可以实现按贡献确定报酬及不同业务场景下的数据价格估算,符合数据要素市场化智能定价的需求,数据要素的市场化定价亟须从经济学范式向智能科学范式演进。Data elements possess unique characteristics such as virtuality,heterogeneity,and non-competitiveness,making traditional pricing theories and methods unsuitable for data element markets.The complexity of data pricing has become a significant barrier to the market-oriented development of data elements.Data transaction serve as the primary way to achieve the marketization of data elements,with price formation replying on both market-based pricing mechanisms and price discovery mechanisms.While traditional economic pricing paradigms are value-based,“data-intelligence empowered”pricing can directly identify relationships among pricing variables and deliver differentiated,personalized dynamic pricing.The intelligent scientific pricing paradigm has evolved through four stages:model-driven,data-driven,dual-driven,and large-model-driven approaches.Pricing strategies grounded in intelligent science not only account for usage scenarios and user heterogeneity but also support compensation-by-contribution mechanisms and cross-scenario data valuation,effectively meeting the demands of market-based intelligent pricing.There is an urgent need for the market-based pricing of data elements to shift from traditional economic pricing paradigms to intelligent scientific paradigms.

关 键 词：数据定价 智能科学 数据要素 数据驱动 大模型 

分 类 号：C93[经济管理—管理学]