Jacobs Hall, Room 2512, Jacobs School of Engineering, 9500 Gilman Dr, La Jolla, San Diego, California 92093
Department of Electrical Engineering
University of Management Sciences (LUMS) Pakistan
The theory of learning in games emerged to understand how repeated interactions among independent individuals can lead to certain equilibrium behaviors in the long run. Stochastic stability is a popular solution concept to explain the long term behavior for a class of learning dynamics called stochastic learning dynamics. In this talk, I will first establish that stochastic stability may not be a complete solution concept for stochastic learning dynamics. Then, I will motivate the need for comparing system behaviors under different stochastic learning rules at short, medium, and long time scales. Finally, I will present a novel framework for the comparative analysis of stochastic learning dynamics at different time scales. To highlight the insights that we can develop based on the proposed framework, I will compare two important learning dynamics namely Log-Linear Learning and Metropolis Learning.
Hassan Jaleel is an Assistant Professor in the Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Pakistan. He received his M.S. and PhD degrees in Electrical and Computer Engineering (ECE) with specialization in Systems and Control from the Georgia Institute of Technology, Atlanta, Georgia. Before joining LUMS, he was a Postdoctoral Research Fellow at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. His research interests lies in the areas of complex networks, game theory, stochastic geometry, and online distributed optimization. He is interested in designing intelligent learning mechanisms with global performance guarantees for self-interested agents in large scale complex networks. Typical application domains of his research include sensor networks, swarm robotics, smart grids, and irrigation networks. Jaleel is a Fulbright alum and a member of IEEE and INFORMS.