Speaker: Vijay Subramanian
Affiliation: University of Michigan
Abstract: This work studies sequential social learning (also known as Bayesian observational learning), and how private communication can enable agents to avoid herding to the wrong action/state. Starting from the seminal BHW (Bikhchandani, Hirshleifer, and Welch, 1992) model where asymptotic learning does not occur, we allow agents to ask private and finite questions to a bounded subset of their predecessors. While retaining the publicly observed history of the agents and their Bayes rationality from the BHW model, we further assume that both the ability to ask questions and the questions themselves are common knowledge. Then interpreting asking questions as partitioning information sets, we study whether asymptotic learning can be achieved with finite capacity questions. Restricting our attention to the network where every agent is only allowed to query her immediate predecessor, an explicit construction shows that a 1-bit question from each agent is enough to enable asymptotic learning.
This is joint work with Shih-Tang Su and Grant Schoenebeck at the University of Michigan. Details of the work can be found at https://arxiv.org/abs/1811.00226 .
Biography: Vijay Subramanian is an Associate Professor in the EECS Department at the University of Michigan. His main research interests are in stochastic modeling, communications, information theory and applied mathematics. A large portion of his past work has been on probabilistic analysis of communication networks, especially analysis of scheduling and routing algorithms. In the past, he has also done some work with applications in immunology and coding of stochastic processes. Prof. Subramanian’s current research interests are on game theoretic and economic modeling of socio-technological systems and networks, and the analysis of associated stochastic processes.
For more information, contact Prof. Suhas Diggavi ()
Date(s) - Mar 05, 2019
1:00 pm - 2:00 pm
E-IV Maxwell Room #57-124
420 Westwood Plaza - 5th Flr. , Los Angeles CA 90095