Google TechTalks | Master Equation for Discrete-Time Stackelberg Mean Field Games @GoogleTechTalks | Uploaded September 2022 | Updated October 2024, 1 week ago.
A Google TechTalk, presented by Deepanshu Vasal, 2022/09/13
A Google Algorithms Seminar - ABSTRACT: Mean field games are a very popular model of dynamic interaction of large scale selfish agents. In this talk, I will consider a general model of discrete-time Stackelberg mean field games with a leader and an infinite number of followers. The leader and the followers each observe types privately that evolve as conditionally independent controlled Markov processes. The leader commits to a dynamic policy and the followers best respond to that policy and each other. Knowing that the followers would play a mean field game based on her policy, the leader chooses a policy that maximizes her reward. We refer to the resulting outcome as a Stackelberg mean field equilibrium (SMFE).
In this talk, I will provide a master equation of this game that allows one to compute all SMFE backward recursively. Based on this framework, I will consider two numerical examples, one on vaccinations in an epidemic and other on technology adoption. This presents a new idea and a framework in solving such games and has vast potential in designing strategies of the government and firms in control of societal level problems including information design, pricing, vaccination costs, blockchains and much more.
About the Speaker:
Deepanshu Vasal is a research scientist in the Department of Electrical and Computer Engineering (ECE) at Northwestern University. He received his PhD from University of Michigan, Ann Arbor in EE:Systems in 2016, and was a postdoc at UT Austin. His current research interests are game theory, multi-agent decision making, and feedback communication. Before that, he received his B.Tech degree in electronics and communication engineering from IIT Guwahati in 2009.
A Google TechTalk, presented by Deepanshu Vasal, 2022/09/13
A Google Algorithms Seminar - ABSTRACT: Mean field games are a very popular model of dynamic interaction of large scale selfish agents. In this talk, I will consider a general model of discrete-time Stackelberg mean field games with a leader and an infinite number of followers. The leader and the followers each observe types privately that evolve as conditionally independent controlled Markov processes. The leader commits to a dynamic policy and the followers best respond to that policy and each other. Knowing that the followers would play a mean field game based on her policy, the leader chooses a policy that maximizes her reward. We refer to the resulting outcome as a Stackelberg mean field equilibrium (SMFE).
In this talk, I will provide a master equation of this game that allows one to compute all SMFE backward recursively. Based on this framework, I will consider two numerical examples, one on vaccinations in an epidemic and other on technology adoption. This presents a new idea and a framework in solving such games and has vast potential in designing strategies of the government and firms in control of societal level problems including information design, pricing, vaccination costs, blockchains and much more.
About the Speaker:
Deepanshu Vasal is a research scientist in the Department of Electrical and Computer Engineering (ECE) at Northwestern University. He received his PhD from University of Michigan, Ann Arbor in EE:Systems in 2016, and was a postdoc at UT Austin. His current research interests are game theory, multi-agent decision making, and feedback communication. Before that, he received his B.Tech degree in electronics and communication engineering from IIT Guwahati in 2009.
