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

合作研究：收入管理和动态定价中的模型准确性和学习

• 批准号: 1033048
• 负责人: Tito Homem-de-Mello
• 金额: $ 3.03万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1033048&HistoricalAwards=false
• 关键词: Collaborative; Research; Model; Accuracy; Learning

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.

这项研究将解决与在收入管理和动态定价背景下决策者（买卖双方）使用的模型准确性有关的问题。 该研究将开发数学框架，其中将研究此类定价过程的长期动态行为。 将重点放在对决策者使用结构不准确的模型应用的积极和负面后果的理解中，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策。 决策者模型的准确性对一些自然绩效指标（例如长期应计收入）的影响将得到分析。 这些研究将涉及一种表征长期性能以及通过计算机模拟实验的理论的发展。如果成功，拟议的工作将有助于收入管理专业人员了解使用不准确模型的后果。 这是一个实际利益的问题，因为在收入管理应用中使用的人类行为模型通常是不准确的。可能受益于这项研究的特定设置包括：（1）试图学习价格和需求之间关系的可替代产品的竞争卖家，例如手机服务的卖家； （2）多个购买者试图了解特定产品可供购买的可能性，例如想要决定早日购买票或等到以后购买票证的折扣机票买家；或（3）一个替代产品的单个卖家，他们试图学习买家如何在产品中进行选择，例如想要知道买家如何在各种交货时间中选择的制造商，这是交货时间的函数。 这项研究的结果可能会提醒从业者考虑学习与决策与决策之间的相互作用以及模型结构对这种相互作用的影响的重要性。 反过来，这可能会导致建立模型的发展，这些模型可以偏离其假设，并最终采用这些行业中更有效的定价方法。