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DTSTART;TZID=Asia/Singapore:20260327T100000
DTEND;TZID=Asia/Singapore:20260327T113000
DTSTAMP:20260419T082952
CREATED:20260325T031142Z
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SUMMARY:DAO-ISEM-IORA Seminar Series: Yael Grushka-Cockayne
DESCRIPTION:Name of Speaker\n\n\nYael Grushka-Cockayne \n\n\n\n\nSchedule \n\n\n27 Mar 2026\, 10am – 11.30am \n (60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nHSS 4-2\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/51hGI1hiRe-T473GjiQA1w\n\n\n\n\nTitle\n\n\nDecision-making with Ordinal Ratings\n\n\n\n\nAbstract \n\n\nExperts often provide judgments on an ordinal scale\, which are easy to generate and are intuitive. Ordinal ratings\, however\, are not trivial to aggregate across multiple experts\, as they provide neither the strict preference ordering of a ranking\, nor the intensity of preference of cardinal scores. In addition\, ordinal rating judgments often map to a broad set of outcomes\, which are not expressed through the ordinal\, discrete set of choices elicited. In this way\, ordinal ratings also neglect to express the degree of uncertainty that may exist when rankings are interpreted as forecasts. We offer a framework for mapping ordinal ratings to continuous outcome distributions\, allowing for the aggregation of ratings and the expression of the uncertainty that may exist in the forecasts. Finally\, our framework allows for rendering the aggregate distributional forecasts back to the original ordinal scale\, providing again an intuitive set of judgements\, to be used by the decision maker. We demonstrate our framework in the context of National Football League (NFL) scout assessments of players performance. These assessments\, treated as forecasts\, are utilized by general managers when making player selection decisions in the annual NFL draft.\n\n\n\n\nAbout the Speaker\n\n\nYael Grushka-Cockayne \nLandmark Communication Incorporated Professor of Business Administration\, Vice Dean and Senior Associate Dean for Professional Degree Programs\, Academic Co-Director of the LaCross Institute for AI.\nProfessor Yael Grushka-Cockayne’s research and teaching activities focus on data science\, artificial intelligence\, forecasting\, project management and behavioral decision-making. Her research is published in numerous academic and professional journals\, and she is a regular speaker at international conferences in the areas of decision analysis\, project management and management science. Prof. Grushka-Cockayne is an award-winning teacher\, winning the Darden Morton Leadership Faculty Award in 2011\, the University of Virginia’s Mead-Colley Award in 2012\, the Darden Outstanding Faculty Award in 2013 and 2022\, University of Virginia All University Teaching Award in 2015\, the Faculty Diversity Award in 2013 and 2018\, and the Transformational Faculty Award in 2024. Prof. Grushka-Cockayne teaches the core “Decision Analysis” course\, an elective she designed on project management\, an elective on data science and a new course on coding with ChatGPT. \nBefore starting her academic career\, she worked in San Francisco as a marketing director of an Israeli ERP company. As an expert in the areas of project management\, Prof. Grushka-Cockayne has served as a consultant to international firms in the aerospace and pharma industries. She is a UVA Excellence in Diversity fellow and a member of INFORMS\, the President of the Decision Analysis Society\, and a member of the Operational Research Society and the Project Management Institute (PMI). She served an associate editor at Management Science and is currently as associate editor at Operation Research. \nGrushka-Cockayne was named one of “21 Thought-Leader Professors” in Data Science. Her course “Fundamentals of Project Planning and Management” Coursera MOOC has over 300\,000 enrolled\, across 200 countries worldwide. Her “Data Science for Business” Harvard Online course\, launched in 2021\, has taught hundreds of learners around the world.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-yael-grushka-cockayne/
CATEGORIES:IORA Seminar Series
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DTSTART;TZID=Asia/Singapore:20260410T100000
DTEND;TZID=Asia/Singapore:20260410T113000
DTSTAMP:20260419T082952
CREATED:20260401T024941Z
LAST-MODIFIED:20260401T024941Z
UID:27574-1775815200-1775820600@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series: Park Sinchaisri
DESCRIPTION:Name of Speaker\n\n\nPark Sinchaisri \n\n\n\n\nSchedule \n\n\n10 Apr 2026\, 10am – 11.30am \n (60 min talk + 30 min Q&A)\n\n\n\nVenue \n\n\nBIZ1 0204\n\n\n\nLink to register \n(via Zoom)\n\nhttps://nus-sg.zoom.us/meeting/register/oo0ElW4xSIu9BcdsAyKQ2A\n\n\n\n\nTitle\n\n\nAlgorithmic Advice\, Human Compliance\, and Learning\n\n\n\n\nAbstract \n\n\nProblem definition:Organizations increasingly deploy algorithmic tools to support complex operational decisions\,raising a practical design question: how should these tools be built when designers care not only about immediate performance\, butalso about preserving and building human skill that remains valuable when advice is unavailable\, imperfect\, or requires genuineoversight? We study how theprecisionof algorithmic advice shapes this trade-off.Methodology/results:We develop a stylized modelof advice-taking and learning. The model characterizes a reward-learning frontier: precise\, action-level advice is easier to implementand improves payoffs while available through higher compliance\, whereas broad\, strategic advice requires interpretation\, inducesgreater exploration\, and generates knowledge that is portable\, even when decision environments differ. We test the model’s predictionsin two online experiments in an electric-vehicle routing and charging task\, representing typical characteristics of sequential decisiontasks. Consistent with the theory\, precise numerical advice delivers the strongest gains during the advice phase\, whereas broaderadvice can yield more robust performance after advice is removed\, specifically if the new environment differs substantially\, butnot completely. We use inverse reinforcement learning to recover interpretable latent objective components from action traces\,distinguishing transient compliance from persistent internalization.Managerial implications:Our results provide design guidancefor advice systems that balance short-run operational efficiency with the development of long-run human capability. They also helpvalidate inverse reinforcement learning as an effective tool for estimating human behaviors in complex sequential tasks\n\n\n\n\nAbout the Speaker\n\n\nPark Sinchaisri is an Assistant Professor of Operations and IT Management at the Haas School of Business\, University of California\, Berkeley. His research draws on operations management\, economics\, machine learning\, and behavioral science to study human decision-making in complex environments\, design human-AI systems that improve decision-making\, and develop strategies for managing the future of work. His work has been published in Management Science and Manufacturing & Service Operations Management\, and has also appeared in leading human-computer interaction venues including CSCW. He received his PhD in Operations\, Information and Decisions and an AM in Statistics from the Wharton School of the University of Pennsylvania\, an SM in Computational Science and Engineering from MIT\, and an ScB in Computer Engineering and Applied Mathematics-Economics from Brown University. Originally from Bangkok\, Thailand\, he hopes his research can help address urban challenges and improve outcomes for marginalized workers.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-park-sinchaisri/
CATEGORIES:IORA Seminar Series
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