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: 0654362
• 负责人: William Cooper
• 金额: $ 16.79万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-15 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0654362&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准确性相关的问题。 该研究将开发数学框架，在其中研究此类定价过程的长期动态行为。 重点将放在理解决策者应用结构不准确的模型的积极和消极后果，决策者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。 将分析决策者模型的准确性对一些自然绩效衡量标准（例如长期应计收入）的影响。 这些研究将涉及描述长期绩效的理论的发展以及通过计算机模拟进行的实验。如果成功，拟议的工作将帮助收益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不准确。可能受益于本研究的具体环境包括：（1）可替代产品的竞争卖家，他们试图了解价格与需求之间的关系，例如移动电话服务的卖家； (2) 多个购买者单独尝试了解特定产品可供购买的概率，例如折扣机票的购买者想要决定是提前购买还是等到稍后购买； (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，例如制造商想要了解买家如何根据交货时间价格在不同的交货时间中进行选择。 这项研究的结果可能提醒实践者考虑学习和决策之间相互作用的重要性，以及模型结构对这种相互作用的影响。 反过来，这可能会导致模型的发展，这些模型对于偏离假设的情况是稳健的，并最终在这些行业中产生更有效的定价方法。