近年来,伴随着互联网技术的快速发展以及电子商务在广大消费者中的日益普及,以淘宝网、eBay等为代表的电子市场的销售规模和交易金额都实现了跳跃式的增长。但是,在买卖双方交易数量快速增加的同时,提供交易平台的电子市场所有者始终缺乏有效的收费模式来实现自身的利润最大化,这一窘境已经得到产业界和学术界越来越多的关注。当前,各类电子市场大多采用的收费模式是以每个卖家前一期的销售历史来预测该卖家下一期的销售数量,并以此作为基准来动态更新电子市场对于该卖家下一期的收费标准。这种收费方法暗含的假设是相邻周期间产品销量为强正相关,但忽略了产品销量因季节性、流行性、替代产品威胁等各类因素影响而导致的更加复杂的中长期变化趋势。针对这一问题,本文为电子市场所有者提供了一类新的个性化动态契约机制。这种契约机制假设相邻周期的产品销量服从联合正态分布,在每个周期开始前,卖家能够充分考虑到各类因素的影响,从而对于下一期销量做出比电子市场所有者更准确的条件预期。但是,卖家对于相邻周期销量间的相关系数的观测属于私有信息,为了诱导卖家披露这一私有信息,电子市场所有者在每周期开始前需要根据该卖家上一周期的实际销售数量向卖家提供一系列契约(a menu of contracts)供卖家选择。本文构建了这种个性化动态契约的参数优化问题。求解结果揭示了一个简单的决策准则,即最优的契约参数可以表示为关于上述相关系数的failure rate的函数。通过一组数值试验,本文将这种契约与其他三类契约进行了对比,验证了这种契约能够充分地利用卖家的私有信息来提升电子市场所有者的利润。本文所述的契约机制在互联网环境下具有较强的可操作性,因此可以为电子市场所有者提供直接的管理参考和决策依据。 In recent years, along with the rapid development of Internet technology and the increasing popularization of e-commerce among consumers, the sales volume and transaction amount of the electronic market represented by Taobao, eBay, etc. have all achieved leapfrog growth. However, as the number of transactions between buyers and sellers has increased rapidly, the owners of electronic marketplaces that provide trading platforms have always lacked effective charging models to maximize their profits. This dilemma has drawn more and more attention from industry and academia . Currently, most types of electronic market charging mode is based on each seller’s previous sales history to predict the seller’s next sales, and as a benchmark to dynamically update the electronic market for the seller’s next issue of Charges. The implicit assumption of this method of charging is that the sales volume of products in the adjacent weeks is strongly and positively correlated but ignores the more complex long-term and medium-term trends resulting from the impact of various factors such as seasonality, popularity and threat of alternative products. In response to this problem, this paper provides a new kind of personalized dynamic contract mechanism for the owners of electronic markets. This contractual mechanism assumes that sales of products in adjacent cycles are subject to a joint normal distribution and that sellers can take full account of the impact of various factors before the start of each cycle to make the next sales volume more accurate than market owners The conditions are expected. However, the sellers’ observations of the correlation coefficient between adjacent cycles are private information. To induce the seller to disclose this private information, the market owner needs to provide the seller with the actual number of sales for the previous period of the seller before the start of each cycle A series of contracts for the seller to choose. This paper constructs this kind of personalized dynamic contract parameter optimization problem. The result of the solution reveals a simple decision rule that the optimal contractual parameters can be expressed as a function of the failure rate of the above correlation coefficient. Through a series of numerical experiments, this paper contrasts this kind of contract with the other three kinds of contracts, and verifies that this kind of contract can make full use of the seller’s private information to enhance the profit of the electronic market owner. The contract mechanism described in this article has strong operability in the Internet environment, so it can provide direct management reference and decision-making basis for the owners of electronic markets.