Fall 2019

Thur, Sept 12th 2019, 1:30 PM, Venue: Panther Hollow, CIC (4101)
Vijay Subramanian, University of Michigan

Title: Social Learning with Questions

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.
Joint work with Shih-Tang Su and Grant Schoenebeck, Univ. of Michigan.

Bio: Vijay Subramanian is an Associate Professor in the EECS Department at the University of Michigan since 2014. After graduating with his Ph.D. from UIUC in 1999, he did a few stints in industry, research institutes and universities in the US and Europe before his current position . 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. His current research interests are on game theoretic and economic modeling of socio-technological systems and networks, and the analysis of associated stochastic processes.