A Robust Framework for Modeling Preferences and its Applications in Revenue Management

偏好建模的鲁棒框架及其在收入管理中的应用

• 批准号: 1636046
• 负责人: Vineet Goyal
• 金额: $ 32.31万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636046&HistoricalAwards=false
• 关键词: Robust; Framework; Modeling; Preferences; its

Modeling customer preferences is a fundamental challenge in estimating demand in revenue management problems since the uncertain demand crucially depends on the substitution behavior of the customers. Such modeling is especially difficult as the preferences are latent and unobservable. The broad goal of this project is to develop a tractable data-driven approach for modeling preferences that is robust to model selection errors, and develop efficient algorithms for related decision problems. This research aims at developing foundational theory for preference modeling that has potential of significant impact in practice. To facilitate the dissemination of this work to maximize societal impact, the PI will focus on: i) training of students through research and integration of the results from this research into core graduate curriculum, ii) increasing the involvement of undergraduate students in research through summer REU projects, iii) increasing the societal impact through outreach programs for local high-schools including Society for Women in Engineering (SWE) and Harlem School Partnership (HSP) for STEM Education with particular focus on increasing the participation of underrepresented minorities and iv) working with industry towards application of this research in practice.The main focus of this project is to study a Markovian framework for modeling preferences. This framework of modeling choice is simple yet very powerful and amenable to strong generalizations to capture a rich class of preference models. The PI aims to consider two broad directions in this project including: i) using the framework of Markov chain transitions to model a rich class of substitution behavior, and ii) using a Markov chain over an exponentially large state space of preferences to model a class of distribution over permutations such as Mallows and maximum entropy distributions more generally. Efficient estimation and optimization algorithms over these models would result in the theoretical foundations for a tractable data-driven approach to choice modeling. With the availability of large amount of data in today's world, such an approach has the potential of significant impact in many applications.

由于不确定的需求依赖于顾客的替代行为，因此顾客偏好建模是收益管理中需求估计的一个基本挑战。这种建模是特别困难的，因为偏好是潜在的和不可观察的。该项目的主要目标是开发一种易于处理的数据驱动方法，用于建模偏好，该方法对模型选择错误具有鲁棒性，并为相关决策问题开发有效的算法。本研究的目的是发展基础理论的偏好建模，在实践中有潜在的重大影响。为了促进这项工作的传播，以最大限度地扩大社会影响，PI将侧重于：i）通过研究和将本研究结果纳入核心研究生课程来培训学生，ii）通过夏季REU项目增加本科生参与研究，iii）通过为当地高中开展的外展计划，包括工程妇女协会（SWE）和哈莱姆学校伙伴关系（HSP），增加社会影响力为STEM教育，特别注重提高代表性不足的少数民族的参与和iv）与工业界合作，将这项研究应用于实践。该项目的主要重点是研究马尔可夫模型偏好框架。这个建模选择框架简单但非常强大，并且易于进行强大的概括，以捕获丰富的偏好模型。PI的目标是在这个项目中考虑两个广泛的方向，包括：i）使用马尔可夫链转换的框架来建模丰富的替代行为，以及ii）使用马尔可夫链在指数级大的偏好状态空间上建模一类分布，例如Mallow和最大熵分布。有效的估计和优化算法，这些模型将导致一个易于处理的数据驱动的方法来选择建模的理论基础。随着当今世界大量数据的可用性，这种方法在许多应用中具有显著影响的潜力。