EAGER: A Graphical Approach for Choice Modeling

EAGER：选择建模的图形方法

• 批准号: 1450848
• 负责人: Sewoong Oh
• 金额: $ 8.79万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450848&HistoricalAwards=false
• 关键词: EAGER; Graphical; Approach; Choice; Modeling

A central problem of interest to operations managers is how to use historical sales data to accurately predict revenues when offering a particular assortment of products. Such predictions are subsequently used in making important business decisions such as assortment planning, new product development, and demand estimation. Choice models are widely used in modeling the underlying customer behavior. Traditional choice models are either too simple to accurately reflect the nature of how people make decisions, or so complex that it is either computationally intractable to fit the model to historical data or to subsequently use it to make business decisions. This EArly-concept Grant for Exploratory Research (EAGER) project studies innovative and novel choice models that are designed to be computationally efficient for the decision problems of interest in revenue management and at the same time have strong predictive power. The results of the research will enable improved business decisions to be made while simultaneously reducing operational costs. It will provide key technologies in important business applications of assortment planning, new product development, brand value evaluation, demand estimation, optimal pricing, and revenue management. It will generate collaborations among the disciplines of operations management, economics, cognitive psychology, and machine learning. The research component is tightly integrated with the education plan, including a graduate course on probabilistic graphical models. Both undergraduate and graduate students will benefit from the research activities by engaging in research and applying the knowledge to solve real world problems.The suboptimal tradeoff of traditional choice models is due to the fact that these models are designed without computational efficiency in mind. In this era of tremendous increase in the scale of data being generated, computational efficiency is of primary concern. This project will build on graph-based probabilistic models such as random walks on graphs and probabilistic graphical models, and will lead to (a) development of new graph-based models for choice modeling designed for computational efficiency; (b) development of new methodologies for learning these models from historical purchase data; (c) development of novel inference algorithms for predicting the customer preferences from these models; and (d) development of new methodologies for solving optimization problems in revenue management with these models. The research will lay foundations of a new graph-based modeling approach for revenue management. The significance and novelty of the work lie in the fact that the design objective of the choice modeling is critically different from the traditional criteria used by economists and cognitive psychologists (such as describing the functional form of the underlying rational decision processes), which does not consider the computational efficiency of solving decision problems in revenue management. In contrast to this, choice models for making decisions based on massive modern datasets should have computational efficiency embedded into the models by design. The theory and models developed in this project will bring together ideas and techniques from probability theory and graph theory to jointly reason about uncertainty and complexity (such as probabilistic graphical models and random walks on graphs) as well as insights and tools from recent advances in revenue management (such as choice modeling using Markov chains). The research has a potential to advance our fundamental understanding in how people make decisions when presented with many options.

运营经理感兴趣的核心问题是如何使用历史销售数据在提供特定产品时准确预测收入。此类预测随后用于做出重要的业务决策，例如分类计划，新产品开发和需求估算。选择模型广泛用于建模潜在的客户行为。传统的选择模型要么太简单，无法准确地反映人们制定决策的性质，要么是如此复杂，以至于它在计算上棘手以适合该模型与历史数据拟合，或者随后使用它来做出业务决策。这项对探索性研究（急切）项目研究的早期概念赠款研究创新和新颖的选择模型，旨在计算对收入管理中感兴趣的决策问题和同时具有强大的预测能力。研究结果将使改进的业务决策能够同时降低运营成本。它将在各种计划，新产品开发，品牌价值评估，需求估算，最佳定价和收入管理的重要业务应用中提供关键技术。它将在运营管理，经济学，认知心理学和机器学习的学科之间建立合作。研究组成部分与教育计划紧密整合，包括有关概率图形模型的研究生课程。本科生和研究生都将通过研究研究并运用知识来解决现实世界问题，从研究活动中受益。传统选择模型的次优折衷是由于这些模型的设计而没有计算效率。在这个生成数据规模巨大增加的时代，计算效率是主要问题。该项目将基于基于图的概率模型，例如在图形上随机步行和概率图形模型，并将导致（a）开发用于计算效率的选择模型的新的基于图的模型； （b）开发从历史购买数据中学习这些模型的新方法； （c）开发用于预测这些模型客户偏好的新型推理算法； （d）开发使用这些模型在收入管理中解决优化问题的新方法。该研究将奠定基于收入管理的新的基于图的建模方法。这项工作的重要性和新颖性在于，选择建模的设计目标与经济学家和认知心理学家使用的传统标准（例如描述基本理性决策过程的功能形式）的传统标准截然不同，这并不考虑在收入管理中解决决策问题的计算效率。与此相比，基于大量现代数据集做出决策的选择模型应通过设计嵌入到模型中的计算效率。该项目中开发的理论和模型将汇集从概率理论和图理论到关于不确定性和复杂性的共同原因（例如概率图形模型和图形上的随机步行），以及收入管理最新进步（例如使用Markov Chains的选择建模）的洞察力和工具。这项研究有可能提高我们在提供许多选择时如何做出决策的基本理解。