Dynamic Revenue Maximization: A Continuous Time Approach
Dynamic Revenue Maximization: A Continuous Time Approach
复制标题
动态收入最大化：连续时间方法
DOI：
10.2139/ssrn.2553408
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
P. Strack
中科院分区：
文献类型：
--
作者：
D. Bergemann;P. Strack
我们描述了在连续时间内重复销售非耐用品的利润最大化机制。每个代理的估值都是私有信息，并且会随时间变化。在契约时，每个代理人都私下观察自己的初始类型，这影响了其估价过程的演变。在利润最大化机制中，分配是扭曲的，有利于具有高初始类型的代理。我们以封闭的形式推导出最优机制，使我们能够比较各种实例中的畸变。讨论了智能体的评估遵循算术/几何布朗运动、Ornstein-Uhlenbeck过程或由贝叶斯学习模型导出的情况。我们表明，根据私人信息和估值过程的性质，扭曲可能随着时间的推移而增加或减少。
We characterize the profit-maximizing mechanism for repeatedly selling a non-durable good in continuous time. The valuation of each agent is private information and changes over time. At the time of contracting every agent privately observes his initial type which influences the evolution of his valuation process. In the profit-maximizing mechanism the allocation is distorted in favor of agents with high initial types. We derive the optimal mechanism in closed form, which enables us to compare the distortion in various examples. The case where the valuation of the agents follows an arithmetic/geometric Brownian motion, Ornstein-Uhlenbeck process, or is derived from a Bayesian learning model are discussed. We show that depending on the nature of the private information and the valuation process the distortion might increase or decrease over time.