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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART;TZID=Asia/Singapore:20221007T100000
DTEND;TZID=Asia/Singapore:20221007T113000
DTSTAMP:20260418T052702
CREATED:20220812T033922Z
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SUMMARY:IORA Seminar Series - Kaidi Yang
DESCRIPTION:Kaidi Yang is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore. He aims to develop efficient and trustworthy algorithms for the design and operation of future mobility systems\, with a particular focus on advances in vehicular technology (e.g.\, connected and automated vehicles\, electric vehicles\, etc.) and shared mobility. Before joining NUS\, he was a postdoctoral scholar with the Autonomous Systems Lab at Stanford University. He received his Ph.D. from ETH Zurich in 2019\, M.Sc. in Control Science and Engineering from Tsinghua University in 2014\, and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University in 2011. \n  \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Hybrid Session)\nhttps://nus-sg.zoom.us/meeting/register/tZYtfuisrjovHNE8qXjzeBwFwSDutMZlaLbu\n\n\nTitle\nOperation of Traditional and Autonomous Mobility-on-Demand\n\n\nAbstract\nThe past decade has witnessed the widespread deployment of Mobility-on-Demand (MoD) services\, such as the ride-hailing services provided by Uber and Grab. One key operational challenge associated with MoD services is the vehicle imbalances due to asymmetric transportation demand: vehicles tend to accumulate in some regions while becoming depleted in others\, giving rise to inefficient operations of the MoD system. We aim to employ emerging automated vehicles (AVs) to improve the operation of MoD systems\, leveraging their capability of being globally coordinated. In the first part of the talk\, we consider the transition period of AV deployment\, whereby an MoD system operates a mixed fleet of AVs and human-driven vehicles (HVs). In such systems\, AVs are centrally coordinated by the operator\, and the HVs might strategically respond to the coordination of AVs. We model such a system using a Stackelberg framework where the MoD operator serves as the leader and human-driven vehicles serve as the followers. We further develop a real-time coordination algorithm for AVs. In the second part of the talk\, we propose a set of reinforcement learning (RL)-based algorithms to improve the efficiency of MoD systems operating a fleet of AVs. We demonstrate that graph neural networks enable RL agents to recover behaviour policies significantly more transferable\, generalisable\, and scalable than policies learned through other approaches. We further improve the generalisability by integrating meta-learning to transfer to unseen scenarios (e.g.\, different cities). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-kaidi-yang/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221028T100000
DTEND;TZID=Asia/Singapore:20221028T113000
DTSTAMP:20260418T052702
CREATED:20220812T034645Z
LAST-MODIFIED:20221025T014948Z
UID:15974-1666951200-1666956600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Asa Palley
DESCRIPTION:Asa Palley is an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University. He develops and studies methods to gather and aggregate expert opinions for use in managerial making. Secondary interests include learning in sequential decision problems\, carbon pricing and investment in renewable generation and storage capacity\, and the application of decision analysis to public policy questions. His work has been published in the journals Management Science\, Experimental Economics\, and Risk Analysis. \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Hybrid Session)\nhttps://nus-sg.zoom.us/meeting/register/tZwpde-tqTkjHtS27eyS1k611RkaNyQDO4DT\n\n\nTitle\nBoosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions\n\n\nAbstract\nA combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge\, a phenomenon called “the wisdom of crowds”. However\, if the judges use shared information when forming their estimates\, the simple average will end up over-emphasizing this common component at the expense of the judges’ private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges’ opinions. Although this information underlies the judges’ individual estimates\, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article\, we propose a weighting of judges’ individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge’s estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges’ responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on CRAN. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-asa-palley/
CATEGORIES:IORA Seminar Series
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