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DTSTART;TZID=America/New_York:20261203T163000
DTEND;TZID=America/New_York:20261203T173000
DTSTAMP:20260811T153908Z
CREATED:20260709T160232Z
LAST-MODIFIED:20260811T153908Z
UID:10003971-1796315400-1796319000@cmsa.fas.harvard.edu
SUMMARY:Geometry and Mathematical Physics seminar
DESCRIPTION:Geometry and Mathematical Physics seminar \nSpeaker: Paul Feehan\, Rutgers University \nTitle: TBA
URL:https://cmsa.fas.harvard.edu/event/dgphys_12326/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Geometry and Mathematical Physics Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261203T163000
DTEND;TZID=America/New_York:20261203T173000
DTSTAMP:20260729T200349Z
CREATED:20260729T200349Z
LAST-MODIFIED:20260729T200349Z
UID:10004029-1796315400-1796319000@cmsa.fas.harvard.edu
SUMMARY:Differential Geometry and Physics Seminar
DESCRIPTION:Differential Geometry and Physics Seminar  \n 
URL:https://cmsa.fas.harvard.edu/event/dgphys_12326-2/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Geometry and Mathematical Physics Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261204T120000
DTEND;TZID=America/New_York:20261204T130000
DTSTAMP:20260729T193405Z
CREATED:20260729T193405Z
LAST-MODIFIED:20260729T193405Z
UID:10004008-1796385600-1796389200@cmsa.fas.harvard.edu
SUMMARY:Member Seminar
DESCRIPTION:Member Seminar \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-12426/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261207T163000
DTEND;TZID=America/New_York:20261207T173000
DTSTAMP:20260729T201051Z
CREATED:20260729T201051Z
LAST-MODIFIED:20260729T201051Z
UID:10004036-1796661000-1796664600@cmsa.fas.harvard.edu
SUMMARY:Colloquium
DESCRIPTION:Colloquium
URL:https://cmsa.fas.harvard.edu/event/colloquium-12726/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
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DTSTART;TZID=America/New_York:20261208T140000
DTEND;TZID=America/New_York:20261208T160000
DTSTAMP:20260902T182726Z
CREATED:20260829T181947Z
LAST-MODIFIED:20260902T182726Z
UID:10004040-1796738400-1796745600@cmsa.fas.harvard.edu
SUMMARY:Mathematical Economics Seminar
DESCRIPTION:Speaker: Karun Adusumilli\, University of Pennsylvania\,\nTitle: Continuous Time Asymptotic Representations for Adaptive Experiments\nAbstract: This article develops a continuous-time asymptotic framework for analyzing adaptive experiments—settings in which data collection and treatment assignment evolve dynamically in response to incoming information. Akey challenge in analyzing fully adaptive experiments\, where the assignment policy is updated after each observation\, is that the sequence of policy rules often lack a well-defined asymptotic limit. To address this\, we focus instead on the empirical allocation process\, which captures the (normalized) number of observations assigned to each treatment over time. We show that\, under general conditions\, any adaptive experiment and its associated empirical allocation process can be approximated by a limit experiment defined by Gaussian diffusions with unknown drifts and a corresponding continuous-time allocation process. This limit representation facilitates the analysis of optimal decision rules by reducing the dimensionality of the state-space and exploiting the tractability of Gaussian diffusions. We apply the framework to derive optimal estimators\, analyze in-sample regret for adaptive experiments\, and construct e-processes for anytime-valid inference. Notably\, we introduce the first definition of any-time and any-experiment valid inference for multi-treatment settings. \nSpeaker: Toru Kitagawa\, Brown University\,\nTitle: TBA \nSpeaker: Chen Qiu\, Cornell University\,\nTitle: Local Asymptotics for Treatment Choice with Partial Identification\nAbstract: We provide a new asymptotic framework to derive approximately optimal treatment assignments when sampling noise from data is compounded by fundamental uncertainty due to partial identification. We recenter the reduced-form parameter around its least-favorable configuration and consider drifting parameter sequences that yield both diminishing levels of sampling uncertainty and of partial identification. We characterize the limiting decision problem as a normal location shift model with a suitable limiting identified set. We apply our results to treatment choice problems with contaminated outcomes\, to robust welfare analyses with partially identified consumer surplus\, and to the problem of aggregating experimental estimates for policy adoption.
URL:https://cmsa.fas.harvard.edu/event/mathecon_12826/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Mathematical Economics Seminar
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