| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 106 === This thesis introduces scenarios for the well-known dynamic pricing problem, and presents corresponding learning algorithms. Different form the previous works, we mainly focus on the scenario that initially, the seller is given a finite inventory, and want to sell them out in a finite period of time. We build two different theoretical models to describe this problem under different concerns. For the first model, the seller observe a context vector of each consumer before deciding the posted price for her, also the context of each consumer is adversarially given. In general, the objective of the seller is to maximize the revenue, however, it’s not as trivial under the adversarial setting with limited inventory. We introduce a criterion to evaluate the performance of an learning algorithm, and then design an algorithm with performance guarantee on top of such criterion. For the second model, all consumers may stay in the market for a period of time, and they may wait for lower payment in order to maximize their utility. In this model, we introduce a new selling mechanism with good properties, and design a learning algorithm with performance guarantee based on the new mechanism.
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