主讲人Speaker: 成子腾   助理教授    香港科技大学（广州）

时间Date & Time: 2025年6月27日（周五），下午14:30--16:00

地点Venue:粤海校区汇星楼565会议室

内容简介/ Abstract:

We investigate a framework for identifying an agent's risk aversion through interactive questioning. First, we study a one-period setting where the agent's risk aversion is characterized by a state-dependent cost function and a distortion risk measure. We establish the quantitative identifiability of this framework, proving that a finite number of interactions suffices to estimate the true risk aversion within a specified accuracy. Next, we analyze question design efficiency to accelerate estimation and derive a theoretical upper bound on convergence. We propose a novel design method based on distinguishing power and evaluate its performance via simulations. Additionally, we extend our analysis to an infinite-horizon setting, incorporating a discount factor to model dynamic risk aversion. Our approach to inferring risk preferences enables personalized robo-advising tailored to individual clients' needs.

主讲人介绍/Biography of the speaker：

成子腾博士于2024年8月加入香港科技大学（广州）金融科技学院，担任助理教授一职。在加入港科广之前，他曾在多伦多大学统计系、在Sebastian Jaimungal教授指导下从事博士后研究工作。成博士在伊利诺伊理工学院应用数学系获得博士学位，师从Tomasz R. Bielecki教授和龚若汀教授。成博士的研究领域涵盖风险规避决策、强化学习、逆强化学习、平均场博弈等方向。