Abstract: At present, various data trading platforms in the market promote the market flow of data products, and also provide a place for potential data demanders to obtain data products or services. However, the price of data products is affected by a variety of subjective and objective factors, which makes it difficult to price data products in the way of traditional commodities. From the perspective of data seller, the same data product can be sold several times, so that it cannot be priced according to the transaction price equal to marginal cost. From the perspective of data buyers, the real value of data products can be defined only after use, and there is a certain value lag. In the face of the lack of historical experience in the pricing process of data products, exploring the pricing mechanism of data products has become the key to promoting the sustainable development of data trading platforms.
Considering the existence of information asymmetry in data transactions, the pricing by data trading platforms can easily lead to unfairness. By analyzing the trading behavior between data trading platforms and potential data demanders, we consider the pricing problem of data transactions as a coordination problem under information asymmetry. In other words, for data products that cannot be traded repeatedly, how does the data trading platform coordinate the purchase volume of many potential data demanders to maximize social welfare? A challenge encountered in the coordination process is that the utility of the potential data demanders is private information, making it rather challenging for the data trading platform to achieve group objectives with incomplete information.
In this paper, we formulate the coordination problem as a mechanism design problem. The data demanders are modeled as individual utility maximizers, while the group objective is encoded in the social choice function, which is to maximize the social welfare subject to a maximum supply constraint. We then design an auction mechanism to determine the optimal trading price of data products. Specifically, the information space of auction mechanism is a function space monotonically decreasing on data trading price, and the result function is determined by the demand functions of the data demander bidding and the maximum supply of the data trading platform. We prove that the proposed auction pricing mechanism can implement the social choice function in dominant strategy equilibrium. Finally, the effectiveness of the designed auction mechanism is further verified by a numerical experiment.
In summary, the research methods proposed in this paper does not require iterative information exchanges between the data trading platform and data demanders, and can be implemented with limited communication resources. The research conclusion can provide theoretical guidance for the data trading platform to set trading prices for data products that cannot be traded repeatedly. In addition, the research methods and conclusions proposed in this paper can be applied to similar trading problems, such as carbon emission trading, water rights trading, etc. The commonality of such problems is that the participants are usually one seller and many buyers, and there is information asymmetry between them.

Key words: data trading, demand information asymmetry, pricing mechanism, auction design

摘要： 针对数据产品呈现双边价值不确定,数据交易平台单方面定价容易造成不公平,如何设定交易双方满意的价格成为推动数据交易平台健康发展的关键问题。通过分析数据交易平台与潜在数据需求者间的交易行为,结合数据产品的特征,构建数据交易双方的收益模型。考虑数据交易双方间存在需求信息不对称及利益冲突,依据机制设计理论和拍卖理论,通过设计拍卖机制来决策最优的数据交易价格。研究发现:拍卖定价机制的信息空间是关于数据交易价格连续单调递减的函数空间,结果函数是由数据需求者投标的需求函数和平台的最大供给量决定。最后,通过算例实验进一步验证了所设计的拍卖定价机制有效性。本文的研究结论可为进行所有权交易的数据产品的价格设定及交易量分配提供理论指导。

关键词: 数据交易, 需求信息不对称, 定价机制, 拍卖设计