Collaborative Research: Model Accuracy and Learning in Revenue Management and Dynamic Pricing

这项研究将解决与决策者(买方和卖方)在收入管理和动态定价背景下使用的模型的准确性有关的问题。这项研究将建立数学框架，在此框架内研究此类定价过程的长期动态行为。重点将是让决策者了解应用结构上不准确的模型的积极和消极后果，他们试图使用数据了解模型参数，并随着时间的推移做出一系列定价或购买决定。将分析决策者模型的准确性对一些自然业绩指标的影响，如长期应计收入。这些研究将涉及开发一种描述长期业绩的理论，并通过计算机模拟进行实验。如果成功，拟议的工作将帮助收入管理专业人员了解使用不准确模型的后果。这是一个具有实际意义的问题，因为收入管理应用程序中使用的人类行为模型通常是不准确的。可能从这项研究中受益的具体情况包括：(1)可替代产品的竞争卖家试图了解价格和需求之间的关系，例如移动电话服务的卖家；(2)多个买家单独试图了解特定产品可供购买的概率，例如折扣机票的买家想要决定是提前购买机票还是等到以后再购买；或(3)可替代产品的单一卖家试图了解买家如何在产品中进行选择，例如制造商想知道买家将如何根据交货期的价格在各种交货期中进行选择。这项研究的结果可能会提醒实践者考虑学习和决策之间交互作用的重要性，以及模型结构对这种交互作用的影响。这反过来可能导致模型的发展，这些模型对偏离他们的假设是稳健的，并最终在这些行业产生更有效的定价方法。

This research will address issues related to the accuracy of models used by decision makers (buyers and sellers) in the context of revenue management and dynamic pricing. The research will develop mathematical frameworks within which the long run dynamic behavior of such pricing processes will be studied. Emphasis will be given to developing an understanding of positive and negative consequences of the application of structurally inaccurate models by decision makers, who attempt to learn about model parameters using data and who make a sequence of pricing or buying decisions over time. The impact of the accuracy of the decision makers' models on some natural performance measures such as long run accrued revenues will be analyzed. These studies will involve the development of a theory characterizing long run performance as well as experimentation via computer simulations.If successful, the proposed work will help revenue management professionals understand the consequences of using inaccurate models. This is an issue of practical interest, since models of human behavior used in revenue management applications are often inaccurate. Specific settings that may benefit from this research include those with: (1) competing sellers of substitutable products who attempt to learn the relationship between prices and demand, such as sellers of mobile phone services; (2) multiple buyers who individually attempt to learn the probability that a particular product will be available for purchase, such as buyers of discount airline tickets who want to decide whether to purchase a ticket early or wait until later; or (3) a single seller of substitutable products who attempts to learn how buyers choose among the products, such as a manufacturer who wants to know how buyers will choose among various lead times as a function of lead times' prices. Results of this research may alert practitioners of the importance of considering interactions between learning and decision-making, and the effect of a model's structure on this interaction. This, in turn, may lead to the development of models that are robust to departures from their assumptions, and ultimately to more effective methods of pricing in these industries.