Spreading (diffusion) of new products is a classical problem. Traditionally, it has been analyzed using the compartmental Bass model, which implicitly assumes that all individuals are homogeneous and connected to each other. To relax these assumptions, research has gradually shifted to the more fundamental Bass model on networks, which is a particle model for the stochastic adoption by each individual.

In this talk I will review the emerging mathematical theory for the Bass model on networks. I will present analytic tools that enable us to obtain explicit expressions for the expected adoption level on various networks (complete, circular, d-regular, Erdos-Renyi, …), without employing mean-field type approximations. The main focus of the talk will be on the effect of network structure. For example, which networks yield the slowest and fastest adoption? What is the effect of boundaries? Of heterogeneity among individuals??.

Gadi Fibich is a Professor of Applied Mathematics at Tel Aviv University. He graduated with a BSc degree in Applied Mathematics from the Technion in 1988, an MSc in Applied Mathematics from the Technion in 1990, and a PhD in Applied Mathematics from the Courant Institute in 1994. He was a CAM assistant professor at UCLA from 1994–97. Since 1997, he has been a faculty member with the Applied Mathematics Department at Tel Aviv University. From 2017–
2021 he served as Head of the School of Mathematical Sciences at Tel Aviv University. Previously, he worked on problems in nonlinear optics, auction theory, and nonlinear pricing. His current research focuses on spreading processes on networks, with applications in marketing (diffusion of innovations) and epidemiology.

A tea will be served in SAS 4104 at 3:30.