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: 0700161
• 负责人: Anton Kleywegt
• 金额: $ 15.83万
• 依托单位: Georgia Tech Research Corporation
• 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 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0700161&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方面使用的模型的准确性有关的问题。 这项研究将开发数学框架内的长期动态行为的定价过程将被研究。 重点将给予发展的积极和消极的影响，决策者的应用结构不准确的模型，谁试图了解模型参数使用数据和谁作出一系列的定价或购买决定随着时间的推移的理解。 将分析决策者模型的准确性对一些自然绩效指标（如长期应计收入）的影响。 这些研究将涉及一个理论的发展，以及通过计算机模拟的实验，如果成功的话，拟议的工作将有助于收入管理专业人士了解使用不准确的模型的后果。 这是一个实际感兴趣的问题，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环境包括：（1）试图了解价格和需求之间关系的可替代产品的竞争销售商，如移动的电话服务的销售商;（2）单独地尝试学习特定产品将可用于购买的概率的多个购买者，例如想决定是早买还是晚买的打折机票的购买者;或（3）可替代产品的单个销售者，其试图了解购买者如何在产品中进行选择，例如制造商想要知道购买者将如何在作为前置时间的价格的函数的各种前置时间中进行选择。 这项研究的结果可能会提醒从业者考虑学习和决策之间的相互作用的重要性，以及模型的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，这些模型对偏离其假设的情况具有鲁棒性，并最终导致这些行业更有效的定价方法。