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DTSTAMP:20240202T161817
DTSTART;TZID=America/Detroit:20240417T160000
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SUMMARY:Workshop / Seminar:Utilizing game theory and deep learning to find optimal policies for large number of agents
DESCRIPTION:In many real-life policy making applications\, the principal (i.e.\, governor or regulator) would like to find optimal policies for a large population of interacting agents who optimize their own objectives in a game theoretical framework. With the motivation of finding optimal policies for large populations\, we start with introducing continuous time Stackelberg mean field game problem between a principal and a large number of agents. In the model\, the agents in the population play a non-cooperative game and choose their controls to optimize their individual objectives while interacting with the principal and the other agents in the society through the population distribution. The principal can influence the resulting mean field game Nash equilibrium through incentives to optimize her own objective. Therefore\, Stackelberg mean field game problems are by their nature bi-level problems where we have an optimal control problem at the principal level and a Nash equilibrium problem at the population level. This bi-level nature creates many efficiency challenges for the implementation of numerical approaches. For this reason\, we will analyze how to rewrite this bi-level problem as a single-level problem and propose a deep learning approach to solve it. Then we will briefly discuss the convergence of the numerical solution where we utilize the single level problem to the solution of the original problem. We will conclude by demonstrating some applications such as the systemic risk model for a regulator and many banks and an optimal contract problem between a project manager and a large number of employees.
UID:112183-21828569@events.umich.edu
URL:https://events.umich.edu/event/112183
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1360
CONTACT:
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DTSTAMP:20240502T123111
DTSTART;TZID=America/Detroit:20240417T163000
DTEND;TZID=America/Detroit:20240417T173000
SUMMARY:Careers / Jobs:Arrowstreet Capital | Research Educational Event
DESCRIPTION:Interested in working in a collaborative\, analytical and data-driven organization? We encourage you to learn more about Arrowstreet Capital by attending our virtual educational session on April 17th!\n\nZoom Information: https://arrowstreetcapital.zoom.us/j/82225797640\nMeeting ID: 822 2579 7640\n\nArrowstreet Capital\, LP is a privately held Boston-basedinvestment firm that manages global equity portfolios for institutional investors around the world. Founded in 1999\, we currently manage approximately $140B for more than 200 clients and have over 400 employees. We are aresearch-driven investment management firm that takes a quantitative\, systematic approach to managing our clients’ portfolios. We aim to deliversustainable alpha and be a trusted fiduciary for our clients.\n\nOur intern program combines theory\, practice and technology and provides significant insights into quantitative investment management. Our goal is for our interns to make tangible and significant contributions to the team\, whilelearning about markets\, software development practices and quantitative research technologies in the context of model-driven asset management.\n
UID:121126-21845858@events.umich.edu
URL:https://events.umich.edu/event/121126
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:
LOCATION:
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240220T181650
DTSTART;TZID=America/Detroit:20240417T170000
DTEND;TZID=America/Detroit:20240417T183000
SUMMARY:Performance:Class Recital at the Blue Llama Jazz Club
DESCRIPTION:The students of Music Theory 436/536\, \"Analytical History of Jazz\,\" bring their semester to a rousing close with a performance of pre-WWII jazz music at the Blue Llama Jazz Club. Cover charge applies.\n\nWith a mix of classical and jazz majors and traditional and nontraditional (for jazz) instruments\, creativity and collaboration will be the order of the day. 
UID:119143-21842261@events.umich.edu
URL:https://events.umich.edu/event/119143
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Culture,In Person,Interdisciplinary,Music
LOCATION:Off Campus Location
CONTACT:
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