时 间：2023年9月21日 16:00-17:00
地 点：普陀校区理科大楼A1514
报告人：戴晓武 加州大学洛杉矶分校 助理教授
主持人：王光辉 华东师范大学 副教授
摘 要：
We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking the two-sided matching market as a running example, we focus on the decentralized setting, where agents do not share their learned preferences with a central authority. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space, and a learning algorithm for preferences that accounts for uncertainty due to the competition among the agents in the market. Under regularity conditions, we show that our estimator of preferences converges at a minimax optimal rate. Given this result, we derive optimal strategies that maximize agents' expected payoffs and we calibrate the uncertain state by taking opportunity costs into account. We also derive an incentive-compatibility property and show that the outcome from the learned strategies has a stability property. Finally, we prove a fairness property that asserts that there exists no justified envy according to the learned strategies.
报告人简介：
戴晓武，加州大学洛杉矶分校统计与数据科学系以及生物统计系助理教授，2019年毕业于美国威斯康星大学麦迪逊分校获统计学博士学位。2019-2022年在加州大学伯克利分校做博士后，导师为迈克尔乔丹教授。其研究领域主要包括机器学习与经济学、动态系统的统计推断、高维非参数统计、脑图像分析与生物医学研究。在JASA、JMLR等期刊发表多篇论文，并担任Stat杂志副主编。