BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251202T170226
DTSTART;TZID=America/Detroit:20251212T160000
DTEND;TZID=America/Detroit:20251212T170000
SUMMARY:Workshop / Seminar:he aggregate-then-evaluate approach to model uncertainty.
DESCRIPTION:In this talk\, we will discuss a new framework for incorporating model uncertainty into decision-making under uncertainty. To develop a mathematical formalism for decision-making\, one usually studies functionals on the space of random variables\, where the interpretation of the functional depends on the problem's context. For example\, functionals can be used to calculate capital requirements in quantitative risk management or to calculate premiums for insurable losses in actuarial science. In practice\, one usually begins by estimating a probabilistic model and using law-invariant functionals. This approach is convenient because the calculation of law-invariant functionals is tractable and leverages the extensive literature on statistical model building. However\, there is often considerable uncertainty about the probabilistic model estimated from the data. To address this model uncertainty\, one often resorts to distributionally robust optimization. This is done by fixing a collection of competing probabilistic models and computing the worst-case value of the law-invariant functional under them. As we will see\, distributionally robust optimization can be seen as an evaluate-then-aggregate (ETA) approach to model uncertainty. This will then motivate us to discuss an aggregate-then-evaluate (ATE) approach. After defining the ATE approach\, we will discuss ways to ensure that the decisions made through it respect normatively appealing properties\, e.g.\, the preference for diversification. Finally\, we will conclude this talk by showing that the ATE approach better supports decision-making when the statistician is a Bayesian. To do this\, we will show why the ETA approach is not the right way to go about decision-making under uncertainty as a Bayesian\, and how the ATE approach does not run into the same issue.
UID:142341-21890571@events.umich.edu
URL:https://events.umich.edu/event/142341
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:Off Campus Location
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20251204T133737
DTSTART;TZID=America/Detroit:20251212T160000
DTEND;TZID=America/Detroit:20251212T170000
SUMMARY:Tours:Public Tour
DESCRIPTION:Join us to learn more about the history of the Clements Library\, its programs\, and collections. Highlights include Benjamin West's iconic painting \"Death of General Wolfe\,\" a Revolutionary War-era trunk that once housed General Thomas Gage's papers\, and the current exhibit\, “For All Ages.”\nArrive at our North Entrance to check-in for your tour. This entrance is accessible and an elevator is available to move between floors.\nWe want to ensure full participation in our events. If an accommodation would promote that\, please let us know.
UID:142383-21890780@events.umich.edu
URL:https://events.umich.edu/event/142383
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:american culture,american history,Exhibit,Exhibition,Free,Fun,In Person,Tour
LOCATION:William Clements Library
CONTACT:
END:VEVENT
END:VCALENDAR