Mixed Discounting

讲座通知

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2026 年 10 月 21 日（星期三）；

上午 10:00 - 11:00


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信息管理与工程学院102室

上海财经大学（第三教学楼西侧）

上海市杨浦区武东路100号

主题

Mixed Discounting

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主讲人

Wenjie Huang

University of Hong Kong

Wenjie Huang is Assistant Professor (Research) in Department of Data and Systems Engineering, The University of Hong Kong (HKU), recruited by HKU-100 Global Recruitment Exercise. He is also member of HKU-Musketeers Foundation Institute of Data Science. He received B.S. degree in Industrial Engineering and Management, with minor in Economics, from Shanghai Jiao Tong University in 2014, and then Ph.D. degree in Industrial Systems Engineering and Management, from National University of Singapore in 2019, supervised by William B. Haskell. Prior to joining HKU, he held joint postdoc positions at School of Data Science, The Chinese University of Hong Kong, Shenzhen and Dept of Decision Sciences, HEC Montréal, Québec, Canada, supervised by Zizhuo Wang and Erick Delage. His research interests span: Risk Analytics and Robust Optimization, Sequential Decision-Making, Data Science/AI for Operations Management (especially applications in sustainability and social good). His research was selected as Finalists of 2025 IEEE CASE Best Application Paper Award, 2nd Place of 2026 IISE Innovative Design Competition (as supervisor) and feature article by IEEE CSS-DES Newsletter.

讲座简介

In this talk, we propose a new principled constructive model for risk preference mapping in infinite-horizon cash flow analysis. Axiomatically motivated, the mixed discounting model prescribes actions that account for both a traditional discounting by scaling the future incomes and a random interruption time for the cash flow. This work shed light on some properties of the new preference model, establishing conditions under which the infinite-horizon risk is finite, and conditions where the mixed discounting model can be seen as either equivalent or providing a bound on the risk perceived by the traditional approach. Finally, two illustrative examples are presented: an optimal stopping problem and optimal insurance pricing application. In both cases, the impacts of employing mixed discounting on the optimal policy, obtained using dynamic programming, are investigated.

This is joint work with Erick Delage (HEC Montreal) and Shanshan Wang (Beihang University).