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UID:20260517T132941EDT-3654GEmbTN@132.216.98.100
DTSTAMP:20260517T172941Z
DESCRIPTION:\n	\n		\n			Virtual Informal Systems Seminar (VISS)\n\n			Centre for Int
 elligent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des
  Decisions (GERAD)\n		\n	\n\n\nSpeaker: Maryam Kamgarpour –  The University o
 f British Columbia\, Canada \n\nWebinar link\n	Webinar ID: 910 7928 6959\n	P
 asscode: VISS\n\nAbstract: Decision-making in multi-agent systems arises i
 n engineering applications ranging from electricity markets to communicati
 on and transportation networks. I discuss decision-making of multiple play
 ers with coupled objectives. In this setting\, a Nash equilibrium is a sta
 ble solution concept\, since no agent finds it profitable to unilaterally 
 deviate from her choice. Due to geographic distance\, privacy concerns\, o
 r simply the scale of these systems\, each player can only base her decisi
 on on local information. I present our algorithm on learning Nash equilibr
 ia in convex games and discuss its convergence.\n\nBio: Maryam Kamgarpour 
 is with the Institute of Génie Mécanique at the School of Engineering at E
 PFL\, Switzerland. She holds a Doctor of Philosophy in Engineering from th
 e University of California\, Berkeley and a Bachelor of Applied Science fr
 om University of Waterloo\, Canada. Her research is on safe decision-makin
 g and control under uncertainty\, game theory and mechanism design\, mixed
  integer and stochastic optimization and control. Her theoretical research
  is motivated by control challenges arising in intelligent transportation 
 networks\, robotics\, power grid systems and healthcare. She is the recipi
 ent of NASA High Potential Individual Award\, NASA Excellence in Publicati
 on Award\, and the European Union (ERC) Starting Grant.\n
DTSTART:20220225T190000Z
DTEND:20220225T200000Z
LOCATION:CA\, ZOOM
SUMMARY:Learning Nash equilibria with partial information 
URL:https://www.mcgill.ca/cim/channels/event/learning-nash-equilibria-parti
 al-information-337077
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