Data pricing plays a crucial role in unlocking the value of data and underpinning data transactions,thereby fostering the growth and innovation of the data market.Given the inherent uncertainty,diversity,and constraints in data valuation,accurately assessing data value stands out as a primary challenge in data pricing.This paper proposes a method that effectively addresses the uncertainty and diversity issues in data pricing.We introduce a binary nonlinear integrated utility function that evaluates data based on both quality and capacity.Incorporating the Stackelberg game model to analyze participant behaviors and employing the Karush-Kuhn-Tucker(KKT)algorithm,our approach achieves optimal pricing for data products.The findings reveal that the optimal capacity,quality,price,and profits of data product providers negatively correlate with the unit purchase cost of raw data,yet show a positive correlation with both the number of consumers and their sensitivity,with a more pronounced correlation with consumer sensitivity.Compared with conventional pricing models based on a univariate utility function,the proposed model demonstrates substantial advantages.
关键词
数据定价/数据产品/数据交易/集成效用/斯塔克伯格博弈模型
Key words
data pricing/data products/data trading/integrated utility/Stackelberg game model
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