多方竞标交易模式下大数据商品竞价机制研究

Research on the Bidding Mechanism of Big Data Products under the Multi-Party Bidding Trading Model

线上数据交易中如何进行买卖双方关系匹配是当前数据交易环节中的重要部分。数据类商品的定价及大数据交易所的官方指导价制定同样是当前数据交易环节的难题,数据类商品不同于寻常商品,其价值关系难以界定,供需关系难以得到精确匹配是当前阻碍数据类商品交易的拦路石。本文以社会福利最大化为目标,通过建立以VCG机制为基础的优化竞标交易模型,在提高了交易双方匹配效率的同时,通过理论分析证明对于交易双方而言“说真话”为弱占优策略,并通过模拟仿真的数据证明了双边竞价VCG机制在交易时间与交易效率两个方面大幅度领先传统交易机制。具有透明化市场价格、防止企业进行小规模抱团对市场价格进行干扰等优点,为第三方机构进行指导价的制定及整顿数据类商品市场乱象打下坚实基础。

How to match the relationship between buyers and sellers in online data trading is an important part of the current data trading process. The pricing of data commodities and the formulation of the official guide price of big data exchanges are also difficult problems in the current data trading link. Data commodities are different from ordinary commodities, their value relationship is difficult to define, and the difficulty of accurate matching of supply and demand is the current obstacle to the trading of data commodities. This paper aims at maximizing social welfare and establishes an optimized bidding transaction model based on the VCG mechanism, which improves the matching efficiency of both parties in the transaction, and proves that “telling the truth” is a weakly dominant strategy for both parties in the transaction through theoretical analysis, and proves through simulation that the bilateral bidding VCG mechanism has a significant advantage in both transaction time and transaction efficiency. It has the advantages of transparent market price, preventing enterprises from interfering with the market price through small-scale grouping, and laying a solid foundation for the third-party organization to formulate the guide price and rectify the chaos in the data commodity market.