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DTSTART;TZID=Asia/Singapore:20260424T100000
DTEND;TZID=Asia/Singapore:20260424T113000
DTSTAMP:20260423T063439
CREATED:20260421T133016Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Yanwei Jia
DESCRIPTION:Name of Speaker\n\n\nYanwei Jia\n\n\n\n\nSchedule \n\n\n24 Apr 2026\, 10am – 11.30am \n (60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1-0302\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/QpOphmoURVCrbzjftZht4g\n\n\n\n\nTitle\n\n\nWhen to Quit a Venture: Normative Theory and Structural Identification of Decoupled Belief and Decision\n\n\n\n\nAbstract \n\n\nUnderstanding how agents learn and make decisions under uncertainty is a fundamental question in many fields\, with applications including real options\, R&D\, and entrepreneurial ventures. The conventional approach formulates this learning process as an optimal stopping problem within a Bayes framework\, assuming agents possess the cognitive sophistication to continuously update their beliefs based on statistical principles\, thereby rigidly locking their decisions to these updated beliefs and forcing a strict\, deterministic threshold rule. This paper develops a continuous-time reinforcement learning framework for sequential experimentation that formally separates beliefs from actions. By decoupling the evaluation and policy processes\, we provide a unifying framework that yields both normative benchmarks and flexible positive dynamics. Normatively\, using the workhorse Gaussian bandit model\, we prove that by properly tuning learning rates\, the framework achieves a logarithmic regret bound\, matching the efficiency of Bayesian rationality. Positively\, the decoupled policy generates distinct and testable predictions\, such as experience-driven\, path-dependent quitting dynamics\, even when the belief is consistent with its Bayesian counterpart. Crucially\, we prove the structural identifiability of these hidden learning dynamics. By utilizing the method of simulated moments\, we demonstrate how this framework can be structurally estimated directly from censored observational field data and extended to general jump-diffusion bandits.\n\n\n\n\nAbout the Speaker\n\n\nYanwei Jia is an assistant professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. He obtained his Ph.D. degree from the National University of Singapore in 2020\, and B.Sc. from Tsinghua University in 2016. Prior to joining CUHK in 2023\, he was an associate research scientist and adjunct assistant professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research interest falls broadly into financial decision-making problems and uses the structural approach to study the decision making and information aggregation mechanism.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-yanwei-jia/
CATEGORIES:IORA Seminar Series
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