第二期论坛
本期论坛将由来自多伦多大学的陈宁远教授分享他与合作者的研究——“The Use of Binary Choice Forests to Model and Estimate Discrete Choices”。近年来人工智能在全球范围内迎来了行业的快速发育期，作为人工智能的代表性技术，机器学习能够大幅度提高管理科学中预测类研究的效果，是人工智能在管理学应用的重要驱动力之一。以数据为导向，从数据中探索规律与趋势，以期达成预测目的管理学研究也随之逐步发展。本期讨论的重点是应用二元选择林对离散选择进行建模与预测，以捕获与处理随机数据来衡量行为特征，推动机器学习在管理科学中的应用。
活动时间
2020年7月17日 08:45-10:30
线上平台：腾讯会议
论坛语言：中文
活动日程
08:45-08:55
线上签到
08:55-09:00
论坛介绍
09:00-10:00
主题演讲
10:00-10:30
提问环节
嘉宾介绍
主讲人陈宁远
Ningyuan Chen （陈宁远）is currently an assistant professor at the Department of Management at the University of Toronto, Mississauga and cross appointed at the Rotman School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. His research interest includes revenue management and dynamic pricing, applied probability, and statistics.
论文简介
The Use of Binary Choice Forests to Model and Estimate Discrete Choices
Abstract
We show the equivalence of discrete choice models and the class of binary choice forests, which are random forests based on binary choice trees. This suggests that standard machine learning techniques based on random forests can serve to estimate discrete choice models with an interpretable output. This is confirmed by our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently, with its splitting criterion capable of recovering preference rank lists. The framework has unique advantages: it can capture behavioral patterns such as irrationality or sequential searches; it handles nonstandard formats of training data that result from aggregation; it can measure product importance based on how frequently a random customer would make decisions depending on the presence of the product; it can also incorporate price information and customer features. Our numerical results show that using random forests to estimate customer choices represented by binary choice forests can outperform the best parametric models in synthetic and real datasets.
主持人介绍
主持人彭一杰
光华管理学院管理科学与信息系统系助理教授，武汉大学学士，复旦大学博士。他的研究兴趣是运筹学、人工智能。
管理科学暑期线上系列学术论坛 (Management Science Summer Webinar Series)由北京大学光华管理学院管理科学与信息系统系主办，将邀请海内外学界专注OM，IS，AI等多领域的知名专家学者，面向各高校教师、学生、及感兴趣的业内和社会人士，就各自的研究领域最新成果进行分享交流。本系列论坛致力于为国内管理科学与信息系统领域的青年学者打造一个探索研究方向、关注现实问题、分享研究心得的知识交流平台，提供更多学习相关领域研究方法的机会，为推动管理科学学术研究的发展做出努力。