December 15, 2023 11:00 AM Singapore (Registration starts at 10:50 AM)
Presentation 1: Limited-Trust in Diffusion of Competing Alternatives Over Social Networks
We consider the diffusion of two alternatives in social networks using a game-theoretic approach. Each individual plays a coordination game with its neighbors repeatedly and decides which to adopt. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and tend to show trust to others to maximize their long-term utility by choosing a suboptimal option with respect to instantaneous payoff. To capture such trust behavior, we deploy limited-trust equilibrium (LTE) in diffusion process. We analyze the convergence of emerging dynamics to equilibrium points using mean-field approximation and study the equilibrium state and the convergence rate of diffusion using absorption probability and expected absorption time of a reduced-size absorbing Markov chain. We also show that the diffusion model on LTE under the best-response strategy can be converted to the well-known linear threshold model. Simulation results show that when agents behave trustworthy, their long-term utility will increase significantly compared to the case when they are solely self-interested. Moreover, the Markov chain analysis provides a good estimate of convergence properties over random networks.
About the Speaker
Vincent Leon is a graduate student in the Department of Industrial and Enterprise Systems Engineering and a graduate research assistant in the Coordinated Science Laboratory at University of Illinois at Urbana-Champaign. Prior to that, he received his degree of Bachelor of Engineering with First Class Honours from The University of Hong Kong. He is pursuing PhD. in Industrial Engineering and supervised by Prof. S. Rasoul Etesami. Vincent’s research interest lies in the area of dynamic game theory and online learning. His thesis research aims to design online learning algorithms that can be efficiently deployed by the agents in networks and dynamic games to optimize decision-making under uncertainty.
Presentation 2: Differentially Private Online Resource Allocation
We consider an online resource allocation problem when the decision-maker wishes to preserve the privacy of incoming arrivals and rewards. We present a family of algorithms based on private mean estimation and primal-dual schemes that achieve optimal utility guarantees. We establish utility and privacy bounds on the behavior of algorithms in this family under minimal assumptions on the arrival process. These results are further corroborated by a set of numerical experiments that demonstrate the tightness of our bounds.
About the Speaker
Apurv is a Postdoctoral Associate at Texas A&M working with Prof. Le Xie and Prof. PR Kumar. His research interest lies in learning and control with applications in power systems. He obtained his PhD from Columbia University and his bachelor’s degree from IIT Kharagpur.
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