We study a finite-horizon dynamic wholesale-price contract between a manufacturer and a retailer, both of whom observe only sales, rather than the true demand. When the retailer stocks out, unmet demand is unobserved, so both parties update a common posterior over the demand distribution from sales data. Each period, the manufacturer sets the wholesale price, the retailer chooses an order quantity, and the public belief state is updated. We characterize Markov perfect equilibria as functions of this public belief. Our main results are as follows: for Weibull demand, we extend the well-known scaling approach to this strategic learning setting, prove the existence of an equilibrium, and reduce computation to a standardized one-parameter recursion; for exponential demand, we show that the equilibrium is unique and computable via a simple backward recursion.

翻译：我们研究制造商与零售商之间的有限期动态批发价格合约，双方仅能观测到销售数据而非真实需求。当零售商出现缺货时，未满足的需求无法被观测，因此双方需基于销售数据共同更新对需求分布的后验估计。在每一周期，制造商设定批发价格，零售商选择订购数量，公共信念状态随之更新。我们将马尔可夫完美均衡刻画为该公共信念的函数。主要研究结果如下：针对威布尔分布需求，我们将经典的标度分析方法扩展至这一策略性学习场景，证明了均衡的存在性，并将计算简化为标准化的单参数递归问题；针对指数分布需求，我们证明了均衡的唯一性，并可通过简单的后向递归算法进行计算。